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Joni Pajarinen

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52 papers
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52

AAAI Conference 2026 Conference Paper

End-Effector Cartesian Velocity Control for Redundant Loader Cranes Using Reinforcement Learning (Abstract Reprint)

  • Abdolreza Taheri
  • Amy Rankka
  • Pelle Gustafsson
  • Joni Pajarinen
  • Reza Ghabcheloo

Loader cranes with multiple actuated joints are complex systems to be operated by humans. Development of advanced assistance functions, such as end-effector velocity control in Cartesian space allows for utilizing the machine to its full speed and potential, wherein actuator limits, load balance, and singularities, as well as other complicated effects are handled by the automated function. To this end, this article provides a reinforcement learning-based policy optimization workflow for training and evaluating controllers using large-scale, parallelized invocations of forward kinematics. Monte Carlo evaluations of the closed-loop model are performed to inspect the stability and performance in the whole operational envelope of the loader crane for safe deployment on real machines. Our approach does not require any explicit inverse-kinematics model and is free from complex or hard-coded actuator limits or objectives. Results of simulations and experiments on a real loader crane are provided to showcase the performance of our approach in comparison to Jacobian inverse-based methods.

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AAAI Conference 2026 Conference Paper

Predicting Video Slot Attention Queries from Random Slot-Feature Pairs

  • Rongzhen Zhao
  • Jian Li
  • Juho Kannala
  • Joni Pajarinen

Unsupervised video Object-Centric Learning (OCL) is promising as it enables object-level scene representation and understanding as we humans do. Mainstream video OCL methods adopt a recurrent architecture: An aggregator aggregates current video frame into object features, termed slots, under some queries; A transitioner transits current slots to queries for the next frame. This is an effective architecture but all existing implementations both (i1) neglect to incorporate next frame features, the most informative source for query prediction, and (i2) fail to learn transition dynamics, the knowledge essential for query prediction. To address these issues, we propose Random Slot-Feature pair for learning Query prediction (RandSF.Q): (t1) We design a new transitioner to incorporate both slots and features, which provides more information for query prediction; (t2) We train the transitioner to predict queries from slot-feature pairs randomly sampled from available recurrences, which drives it to learn transition dynamics. Experiments on scene representation demonstrate that our method surpass existing video OCL methods significantly, e.g., up to 10 points on object discovery, setting new state-of-the-art. Such superiority also benefits downstream tasks like scene understanding.

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AAAI Conference 2025 Conference Paper

AgentMixer: Multi-Agent Correlated Policy Factorization

  • Zhiyuan Li
  • Wenshuai Zhao
  • Lijun Wu
  • Joni Pajarinen

In multi-agent reinforcement learning, centralized training with decentralized execution (CTDE) methods typically assumes that agents make decisions based on their local observations independently, which may not lead to a correlated joint policy with coordination. Coordination can be explicitly encouraged during training and individual policies can be trained to imitate the correlated joint policy. However, this may lead to an asymmetric learning failure due to the observation mismatch between the joint and individual policies. Inspired by the concept of correlated equilibrium, we introduce a strategy modification called AgentMixer that allows agents to correlate their policies. AgentMixer combines individual partially observable policies into a joint fully observable policy non-linearly. To enable decentralized execution, we introduce Individual-Global-Consistency to guarantee mode consistency during joint training of the centralized and decentralized policies and prove that AgentMixer converges to an ϵ-approximate Correlated Equilibrium. In the Multi-Agent MuJoCo, SMAC-v2, Matrix Game, and Predator-Prey benchmarks, AgentMixer outperforms or matches state-of-the-art methods.

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ICLR Conference 2025 Conference Paper

Discrete Codebook World Models for Continuous Control

  • Aidan Scannell
  • Mohammadreza Nakhaeinezhadfard
  • Kalle Kujanpää
  • Yi Zhao 0014
  • Kevin Sebastian Luck
  • Arno Solin
  • Joni Pajarinen

In reinforcement learning (RL), world models serve as internal simulators, enabling agents to predict environment dynamics and future outcomes in order to make informed decisions. While previous approaches leveraging discrete latent spaces, such as DreamerV3, have demonstrated strong performance in discrete action settings and visual control tasks, their comparative performance in state-based continuous control remains underexplored. In contrast, methods with continuous latent spaces, such as TD-MPC2, have shown notable success in state-based continuous control benchmarks. In this paper, we demonstrate that modeling discrete latent states has benefits over continuous latent states and that discrete codebook encodings are more effective representations for continuous control, compared to alternative encodings, such as one-hot and label-based encodings. Based on these insights, we introduce DCWM: Discrete Codebook World Model, a self-supervised world model with a discrete and stochastic latent space, where latent states are codes from a codebook. We combine DCWM with decision-time planning to get our model-based RL algorithm, named DC-MPC: Discrete Codebook Model Predictive Control, which performs competitively against recent state-of-the-art algorithms, including TD-MPC2 and DreamerV3, on continuous control benchmarks.

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ICRA Conference 2025 Conference Paper

Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving

  • Kalle Kujanpää
  • Daulet Baimukashev
  • Farzeen Munir
  • Shoaib Azam
  • Tomasz Piotr Kucner
  • Joni Pajarinen
  • Ville Kyrki

Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversity and variance. We address this challenge by proposing a novel approach that leverages contrastive learning to extract a dictionary of driving styles from pre-existing human driving data. We discretize these styles with quantization, and the styles are used to learn a conditional diffusion policy for simulating human drivers. Our empirical evaluation confirms that the behaviors generated by our approach are both safer and more human-like than those of the machine-learning-based baseline methods. We believe this has the potential to enable higher realism and more effective techniques for evaluating and improving the performance of autonomous vehicles.

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AAAI Conference 2025 Conference Paper

Entropy Regularized Task Representation Learning for Offline Meta-Reinforcement Learning

  • Mohammadreza Nakhaeinezhadfard
  • Aidan Scannell
  • Joni Pajarinen

Offline meta-reinforcement learning aims to equip agents with the ability to rapidly adapt to new tasks by training on data from a set of different tasks. Context-based approaches utilize a history of state-action-reward transitions – referred to as the context – to infer a representation of the current task, and then condition the agent, i.e., the policy and value function, on this task representation. Intuitively, the better the task representation captures the underlying tasks, the better the agent can generalize to new tasks. Unfortunately, context-based approaches suffer from distribution mismatch, as the context in the offline data does not match the context at test time, limiting their ability to generalize to the test task. This leads to the task representation overfitting to the offline training data. Intuitively, the task representation should be independent of the behavior policy used to collect the offline data. To address this issue, we approximately minimize the mutual information between the distribution over the task representation and behavior policy by maximizing the entropy of behavior policy conditioned on the task representation. We validate our approach in MuJoCo environments, showing that compared to baselines, our task representation more faithfully represents the underlying tasks, leading to outperforming prior methods in both in-distribution and out-of-distribution tasks.

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ICML Conference 2025 Conference Paper

Hierarchical Reinforcement Learning with Uncertainty-Guided Diffusional Subgoals

  • Vivienne Huiling Wang
  • Tinghuai Wang
  • Joni Pajarinen

Hierarchical reinforcement learning (HRL) learns to make decisions on multiple levels of temporal abstraction. A key challenge in HRL is that the low-level policy changes over time, making it difficult for the high-level policy to generate effective subgoals. To address this issue, the high-level policy must capture a complex subgoal distribution while also accounting for uncertainty in its estimates. We propose an approach that trains a conditional diffusion model regularized by a Gaussian Process (GP) prior to generate a complex variety of subgoals while leveraging principled GP uncertainty quantification. Building on this framework, we develop a strategy that selects subgoals from both the diffusion policy and GP’s predictive mean. Our approach outperforms prior HRL methods in both sample efficiency and performance on challenging continuous control benchmarks.

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ICML Conference 2025 Conference Paper

Learning Progress Driven Multi-Agent Curriculum

  • Wenshuai Zhao
  • ZhiYuan Li
  • Joni Pajarinen

The number of agents can be an effective curriculum variable for controlling the difficulty of multi-agent reinforcement learning (MARL) tasks. Existing work typically uses manually defined curricula such as linear schemes. We identify two potential flaws while applying existing reward-based automatic curriculum learning methods in MARL: (1) The expected episode return used to measure task difficulty has high variance; (2) Credit assignment difficulty can be exacerbated in tasks where increasing the number of agents yields higher returns which is common in many MARL tasks. To address these issues, we propose to control the curriculum by using a TD-error based learning progress measure and by letting the curriculum proceed from an initial context distribution to the final task specific one. Since our approach maintains a distribution over the number of agents and measures learning progress rather than absolute performance, which often increases with the number of agents, we alleviate problem (2). Moreover, the learning progress measure naturally alleviates problem (1) by aggregating returns. In three challenging sparse-reward MARL benchmarks, our approach outperforms state-of-the-art baselines.

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IROS Conference 2025 Conference Paper

Manipulate-To-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors

  • Yuying Zhang
  • Joni Pajarinen

Mobile manipulation in dynamic environments is challenging due to movable obstacles blocking the robot’s path. Traditional methods, which treat navigation and manipulation as separate tasks, often fail in such "manipulate-to-navigate" scenarios, as obstacles must be removed before navigation. In these cases, active interaction with the environment is required to clear obstacles while ensuring sufficient space for movement. To address the manipulate-to-navigate problem, we propose a reinforcement learning-based approach for learning manipulation actions that facilitate subsequent navigation. Our method combines manipulability priors to focus the robot on high manipulability body positions with affordance maps for selecting high-quality manipulation actions. By focusing on feasible and meaningful actions, our approach reduces unnecessary exploration and allows the robot to learn manipulation strategies more effectively. We present two new manipulate-to-navigate simulation tasks called Reach and Door with the Boston Dynamics Spot robot. The first task tests whether the robot can select a good hand position in the target area such that the robot base can move effectively forward while keeping the end effector position fixed. The second task requires the robot to move a door aside in order to clear the navigation path. Both of these tasks need first manipulation and then navigating the base forward. Results show that our method allows a robot to effectively interact with and traverse dynamic environments. Finally, we transfer the learned policy to a real Boston Dynamics Spot robot, which successfully performs the Reach task.

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NeurIPS Conference 2025 Conference Paper

MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning

  • Hongjia Liu
  • Rongzhen Zhao
  • Haohan Chen
  • Joni Pajarinen

Learning object-level, structured representations is widely regarded as a key to better generalization in vision and underpins the design of next-generation Pre-trained Vision Models (PVMs). Mainstream Object-Centric Learning (OCL) methods adopt Slot Attention or its variants to iteratively aggregate objects' super-pixels into a fixed set of query feature vectors, termed slots. However, their reliance on a static slot count leads to an object being represented as multiple parts when the number of objects varies. We introduce MetaSlot, a plug-and-play Slot Attention variant that adapts to variable object counts. MetaSlot (i) maintains a codebook that holds prototypes of objects in a dataset by vector-quantizing the resulting slot representations; (ii) removes duplicate slots from the traditionally aggregated slots by quantizing them with the codebook; and (iii) injects progressively weaker noise into the Slot Attention iterations to accelerate and stabilize the aggregation. MetaSlot is a general Slot Attention variant that can be seamlessly integrated into existing OCL architectures. Across multiple public datasets and tasks--including object discovery and recognition--models equipped with MetaSlot achieve significant performance gains and markedly interpretable slot representations, compared with existing Slot Attention variants. The code is available at https: //github. com/lhj-lhj/MetaSlot.

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ICML Conference 2025 Conference Paper

Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport

  • Tuan Dam
  • Pascal Stenger
  • Lukas Schneider
  • Joni Pajarinen
  • Carlo D'Eramo
  • Odalric-Ambrym Maillard

This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) tailored for highly stochastic and partially observable Markov decision processes. We adopt a probabilistic approach, modeling both value and action-value nodes as Gaussian distributions, to introduce a novel backup operator that computes value nodes as the Wasserstein barycenter of their action-value children nodes; thus, propagating the uncertainty of the estimate across the tree to the root node. We study our novel backup operator when using a novel combination of $L^1$-Wasserstein barycenter with $\alpha$-divergence, by drawing a crucial connection to the generalized mean backup operator. We complement our probabilistic backup operator with two sampling strategies, based on optimistic selection and Thompson sampling, obtaining our Wasserstein MCTS algorithm. We provide theoretical guarantees of asymptotic convergence of $\mathcal{O}(n^{-1/2})$, with $n$ as the number of visited trajectories, to the optimal policy and an empirical evaluation on several stochastic and partially observable environments, where our approach outperforms well-known related baselines.

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ICLR Conference 2025 Conference Paper

Multi-Scale Fusion for Object Representation

  • Rongzhen Zhao
  • Vivienne Huiling Wang
  • Juho Kannala
  • Joni Pajarinen

Representing images or videos as object-level feature vectors, rather than pixel-level feature maps, facilitates advanced visual tasks. Object-Centric Learning (OCL) primarily achieves this by reconstructing the input under the guidance of Variational Autoencoder (VAE) intermediate representation to drive so-called slots to aggregate as much object information as possible. However, existing VAE guidance does not explicitly address that objects can vary in pixel sizes while models typically excel at specific pattern scales. We propose Multi-Scale Fusion (MSF) to enhance VAE guidance for OCL training. To ensure objects of all sizes fall within VAE's comfort zone, we adopt the image pyramid, which produces intermediate representations at multiple scales; To foster scale-invariance/variance in object super-pixels, we devise inter/intra-scale fusion, which augments low-quality object super-pixels of one scale with corresponding high-quality super-pixels from another scale. On standard OCL benchmarks, our technique improves mainstream methods, including state-of-the-art diffusion-based ones. The source code is available on https://github.com/Genera1Z/MultiScaleFusion.

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JAAMAS Journal 2025 Journal Article

Open-ended coordination for multi-agent systems using modular open policies

  • David Rother
  • Joni Pajarinen
  • Thomas H. Weisswange

Abstract Significant multi-agent advances addressing the challenge of learning policies for acting in ad hoc teamwork have been made. In ad hoc teamwork, a team of agents must cooperate effectively without prior coordination or communication. Many existing approaches, however, struggle to perform well in open environments where the setting can change significantly during deployment. This paper presents a new reinforcement learning approach to tackle collaboration in open environments controlling one agent with a changing number of distinct other agents, each with an individual task. The approach uses policy blending based on an online goal inference module and a collection of learned policies modeling the individual interaction impact between the agent and populations of partners with different tasks. Blending is done using the estimated goals of others and a posterior-based action blending with entropy adjustment and regularization. Our approach addresses issues of existing policy blending mechanisms, such as handling conflicting modes in action distributions leading to oscillation and instability and adapting to uncertain states dynamically. In experiments in two collaborative open environments based on Overcooked and Level-based Foraging, our approach outperforms a baseline learner, trained with the joint reward of all agents, across changes to both agents and tasks. Ablation studies further highlight the importance of our posterior-based blending mechanism to achieve high rewards as well as the provided goal weighting. The proposed approach provides an important step towards the application of reinforcement learning to AI assistance beyond strictly closed worlds and towards more realistic scenarios.

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JAIR Journal 2024 Journal Article

A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search

  • Tuan Dam
  • Carlo D'Eramo
  • Jan Peters
  • Joni Pajarinen

Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex decisionmaking problems through the synergy of Monte-Carlo planning and Reinforcement Learning (RL). The highly combinatorial nature of the problems commonly addressed by MCTS requires the use of efficient exploration strategies for navigating the planning tree and quickly convergent value backup methods. These crucial problems are particularly evident in recent advances that combine MCTS with deep neural networks for function approximation. In this work, we propose two methods for improving the convergence rate and exploration based on a newly introduced backup operator and entropy regularization. We provide strong theoretical guarantees to bound convergence rate, approximation error, and regret of our methods. Moreover, we introduce a mathematical framework based on the use of the α-divergence for backup and exploration in MCTS. We show that this theoretical formulation unifies different approaches, including our newly introduced ones, under the same mathematical framework, allowing to obtain different methods by simply changing the value of α. In practice, our unified perspective offers a flexible way to balance between exploration and exploitation by tuning the single α parameter according to the problem at hand. We validate our methods through a rigorous empirical study from basic toy problems to the complex Atari games, and including both MDP and POMDP problems.

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AAAI Conference 2024 Conference Paper

Backpropagation Through Agents

  • Zhiyuan Li
  • Wenshuai Zhao
  • Lijun Wu
  • Joni Pajarinen

A fundamental challenge in multi-agent reinforcement learning (MARL) is to learn the joint policy in an extremely large search space, which grows exponentially with the number of agents. Moreover, fully decentralized policy factorization significantly restricts the search space, which may lead to sub-optimal policies. In contrast, the auto-regressive joint policy can represent a much richer class of joint policies by factorizing the joint policy into the product of a series of conditional individual policies. While such factorization introduces the action dependency among agents explicitly in sequential execution, it does not take full advantage of the dependency during learning. In particular, the subsequent agents do not give the preceding agents feedback about their decisions. In this paper, we propose a new framework Back-Propagation Through Agents (BPTA) that directly accounts for both agents' own policy updates and the learning of their dependent counterparts. This is achieved by propagating the feedback through action chains. With the proposed framework, our Bidirectional Proximal Policy Optimisation (BPPO) outperforms the state-of-the-art methods. Extensive experiments on matrix games, StarCraftII v2, Multi-agent MuJoCo, and Google Research Football demonstrate the effectiveness of the proposed method.

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AAMAS Conference 2024 Conference Paper

Continuous Monte Carlo Graph Search

  • Kalle Kujanpää
  • Amin Babadi
  • Yi Zhao
  • Juho Kannala
  • Alexander Ilin
  • Joni Pajarinen

Online planning is crucial for high performance in many complex sequential decision-making tasks. Monte Carlo Tree Search (MCTS) employs a principled mechanism for trading off exploration for exploitation for efficient online planning, and it outperforms comparison methods in many discrete decision-making domains such as Go, Chess, and Shogi. Subsequently, extensions of MCTS to continuous domains have been developed. However, the inherent high branching factor and the resulting explosion of the search tree size are limiting the existing methods. To address this problem, we propose Continuous Monte Carlo Graph Search (CMCGS), an extension of MCTS to online planning in environments with continuous state and action spaces. CMCGS takes advantage of the insight that, during planning, sharing the same action policy between several states can yield high performance. To implement this idea, at each time step, CMCGS clusters similar states into a limited number of stochastic action bandit nodes, which produce a layered directed graph instead of an MCTS search tree. Experimental evaluation shows that CMCGS outperforms comparable planning methods in several complex continuous DeepMind Control Suite benchmarks and 2D navigation and exploration tasks with limited sample budgets. Furthermore, CMCGS can be scaled up through parallelization, and it outperforms the Cross-Entropy Method (CEM) in continuous control with learned dynamics models.

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ICLR Conference 2024 Conference Paper

Function-space Parameterization of Neural Networks for Sequential Learning

  • Aidan Scannell
  • Riccardo Mereu
  • Paul Edmund Chang
  • Ella Tamir
  • Joni Pajarinen
  • Arno Solin

Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and handling rich inputs, such as images. To address these issues, we introduce a technique that converts neural networks from weight space to function space, through a dual parameterization. Our parameterization offers: (*i*) a way to scale function-space methods to large data sets via sparsification, (*ii*) retention of prior knowledge when access to past data is limited, and (*iii*) a mechanism to incorporate new data without retraining. Our experiments demonstrate that we can retain knowledge in continual learning and incorporate new data efficiently. We further show its strengths in uncertainty quantification and guiding exploration in model-based RL. Further information and code is available on the project website.

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ICML Conference 2024 Conference Paper

Optimistic Multi-Agent Policy Gradient

  • Wenshuai Zhao
  • Yi Zhao 0014
  • ZhiYuan Li
  • Juho Kannala
  • Joni Pajarinen

Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other agents. No methods have been proposed for addressing RO in multi-agent policy gradient (MAPG) methods although these methods produce state-of-the-art results. To address this gap, we propose a general, yet simple, framework to enable optimistic updates in MAPG methods that alleviate the RO problem. Our approach involves clipping the advantage to eliminate negative values, thereby facilitating optimistic updates in MAPG. The optimism prevents individual agents from quickly converging to a local optimum. Additionally, we provide a formal analysis to show that the proposed method retains optimality at a fixed point. In extensive evaluations on a diverse set of tasks including the Multi-agent MuJoCo and Overcooked benchmarks, our method outperforms strong baselines on 13 out of 19 tested tasks and matches the performance on the rest.

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ICML Conference 2024 Conference Paper

Probabilistic Subgoal Representations for Hierarchical Reinforcement Learning

  • Vivienne Huiling Wang
  • Tinghuai Wang
  • Wenyan Yang
  • Joni-Kristian Kämäräinen
  • Joni Pajarinen

In goal-conditioned hierarchical reinforcement learning (HRL), a high-level policy specifies a subgoal for the low-level policy to reach. Effective HRL hinges on a suitable subgoal representation function, abstracting state space into latent subgoal space and inducing varied low-level behaviors. Existing methods adopt a subgoal representation that provides a deterministic mapping from state space to latent subgoal space. Instead, this paper utilizes Gaussian Processes (GPs) for the first probabilistic subgoal representation. Our method employs a GP prior on the latent subgoal space to learn a posterior distribution over the subgoal representation functions while exploiting the long-range correlation in the state space through learnable kernels. This enables an adaptive memory that integrates long-range subgoal information from prior planning steps allowing to cope with stochastic uncertainties. Furthermore, we propose a novel learning objective to facilitate the simultaneous learning of probabilistic subgoal representations and policies within a unified framework. In experiments, our approach outperforms state-of-the-art baselines in standard benchmarks but also in environments with stochastic elements and under diverse reward conditions. Additionally, our model shows promising capabilities in transferring low-level policies across different tasks.

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RLC Conference 2024 Conference Paper

ROER: Regularized Optimal Experience Replay

  • Changling Li
  • Zhang-Wei Hong
  • Pulkit Agrawal
  • Divyansh Garg
  • Joni Pajarinen

Experience replay serves as a key component in the success of online reinforcement learning (RL). Prioritized experience replay (PER) reweights experiences by the temporal difference (TD) error empirically enhancing the performance. However, few works have explored the motivation of using TD error. In this work, we provide an alternative perspective on TD-error-based reweighting. We show the connections between the experience prioritization and occupancy optimization. By using a regularized RL objective with $f-$divergence regularizer and employing its dual form, we show that an optimal solution to the objective is obtained by shifting the distribution of off-policy data in the replay buffer towards the on-policy optimal distribution using TD-error-based occupancy ratios. Our derivation results in a new pipeline of TD error prioritization. We specifically explore the KL divergence as the regularizer and obtain a new form of prioritization scheme, the regularized optimal experience replay (ROER). We evaluate the proposed prioritization scheme with the Soft Actor-Critic (SAC) algorithm in continuous control MuJoCo and DM Control benchmark tasks where our proposed scheme outperforms baselines in 6 out of 11 tasks while the results of the rest match with or do not deviate far from the baselines. Further, using pretraining, ROER achieves noticeable improvement on difficult Antmaze environment where baselines fail, showing applicability to offline-to-online fine-tuning.

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RLJ Journal 2024 Journal Article

ROER: Regularized Optimal Experience Replay

  • Changling Li
  • Zhang-Wei Hong
  • Pulkit Agrawal
  • Divyansh Garg
  • Joni Pajarinen

Experience replay serves as a key component in the success of online reinforcement learning (RL). Prioritized experience replay (PER) reweights experiences by the temporal difference (TD) error empirically enhancing the performance. However, few works have explored the motivation of using TD error. In this work, we provide an alternative perspective on TD-error-based reweighting. We show the connections between the experience prioritization and occupancy optimization. By using a regularized RL objective with $f-$divergence regularizer and employing its dual form, we show that an optimal solution to the objective is obtained by shifting the distribution of off-policy data in the replay buffer towards the on-policy optimal distribution using TD-error-based occupancy ratios. Our derivation results in a new pipeline of TD error prioritization. We specifically explore the KL divergence as the regularizer and obtain a new form of prioritization scheme, the regularized optimal experience replay (ROER). We evaluate the proposed prioritization scheme with the Soft Actor-Critic (SAC) algorithm in continuous control MuJoCo and DM Control benchmark tasks where our proposed scheme outperforms baselines in 6 out of 11 tasks while the results of the rest match with or do not deviate far from the baselines. Further, using pretraining, ROER achieves noticeable improvement on difficult Antmaze environment where baselines fail, showing applicability to offline-to-online fine-tuning.

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EWRL Workshop 2023 Workshop Paper

A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search

  • Tuan Quang Dam
  • Carlo D'Eramo
  • Joni Pajarinen
  • Jan Peters

Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex decision-making problems through the synergy of Monte-Carlo planning and Reinforcement Learning (RL). The highly combinatorial nature of the problems commonly addressed by MCTS requires the use of efficient exploration strategies for navigating the planning tree and quickly convergent value backup methods. These crucial problems are particularly evident in recent advances that combine MCTS with deep neural networks for function approximation. In this work, we introduce a mathematical framework based on using the $\alpha$-divergence for backup and exploration in MCTS. We show that this theoretical formulation unifies different approaches, including our newly introduced ones (Power-UCT and E3W), under the same mathematical framework, allowing us to obtain different methods by simply changing the value of $\alpha$. In practice, our unified perspective offers a flexible way to balance exploration and exploitation by tuning the single $\alpha$ parameter according to the problem at hand. We validate our methods through a rigorous empirical study of a basic toy task Synthetic Tree problem.

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NeurIPS Conference 2023 Conference Paper

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets

  • Zhang-Wei Hong
  • Aviral Kumar
  • Sathwik Karnik
  • Abhishek Bhandwaldar
  • Akash Srivastava
  • Joni Pajarinen
  • Romain Laroche
  • Abhishek Gupta

Offline reinforcement learning (RL) enables learning a decision-making policy without interaction with the environment. This makes it particularly beneficial in situations where such interactions are costly. However, a known challenge for offline RL algorithms is the distributional mismatch between the state-action distributions of the learned policy and the dataset, which can significantly impact performance. State-of-the-art algorithms address it by constraining the policy to align with the state-action pairs in the dataset. However, this strategy struggles on datasets that predominantly consist of trajectories collected by low-performing policies and only a few trajectories from high-performing ones. Indeed, the constraint to align with the data leads the policy to imitate low-performing behaviors predominating the dataset. Our key insight to address this issue is to constrain the policy to the policy that collected the good parts of the dataset rather than all data. To this end, we optimize the importance sampling weights to emulate sampling data from a data distribution generated by a nearly optimal policy. Our method exhibits considerable performance gains (up to five times better) over the existing approaches in state-of-the-art offline RL algorithms over 72 imbalanced datasets with varying types of imbalance.

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ICML Conference 2023 Conference Paper

Hierarchical Imitation Learning with Vector Quantized Models

  • Kalle Kujanpää
  • Joni Pajarinen
  • Alexander Ilin

The ability to plan actions on multiple levels of abstraction enables intelligent agents to solve complex tasks effectively. However, learning the models for both low and high-level planning from demonstrations has proven challenging, especially with higher-dimensional inputs. To address this issue, we propose to use reinforcement learning to identify subgoals in expert trajectories by associating the magnitude of the rewards with the predictability of low-level actions given the state and the chosen subgoal. We build a vector-quantized generative model for the identified subgoals to perform subgoal-level planning. In experiments, the algorithm excels at solving complex, long-horizon decision-making problems outperforming state-of-the-art. Because of its ability to plan, our algorithm can find better trajectories than the ones in the training set.

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NeurIPS Conference 2023 Conference Paper

Hybrid Search for Efficient Planning with Completeness Guarantees

  • Kalle Kujanpää
  • Joni Pajarinen
  • Alexander Ilin

Solving complex planning problems has been a long-standing challenge in computer science. Learning-based subgoal search methods have shown promise in tackling these problems, but they often suffer from a lack of completeness guarantees, meaning that they may fail to find a solution even if one exists. In this paper, we propose an efficient approach to augment a subgoal search method to achieve completeness in discrete action spaces. Specifically, we augment the high-level search with low-level actions to execute a multi-level (hybrid) search, which we call complete subgoal search. This solution achieves the best of both worlds: the practical efficiency of high-level search and the completeness of low-level search. We apply the proposed search method to a recently proposed subgoal search algorithm and evaluate the algorithm trained on offline data on complex planning problems. We demonstrate that our complete subgoal search not only guarantees completeness but can even improve performance in terms of search expansions for instances that the high-level could solve without low-level augmentations. Our approach makes it possible to apply subgoal-level planning for systems where completeness is a critical requirement.

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ICRA Conference 2023 Conference Paper

Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation

  • Wenyan Yang
  • Alexandre Angleraud
  • Roel Pieters
  • Joni Pajarinen
  • Joni-Kristian Kämäräinen

Robot control for tactile feedback based manip-ulation can be difficult due to modeling of physical contacts, partial observability of the environment, and noise in perception and control. This work focuses on solving partial observability of contact-rich manipulation tasks as a Sequence-to-Sequence (Seq2Seq) Imitation Learning (IL) problem. The proposed Seq2Seq model first produces a robot-environment interaction sequence to estimate the partially observable environment state variables, and then, the observed interaction sequence is transformed to a control sequence for the task itself. The proposed Seq2Seq IL for tactile feedback based manipulation is experimentally validated on a door-open task in a simulated environment and a snap-on insertion task with a real robot. The model is able to learn both tasks from only 50 expert demonstrations while state-of-the-art reinforcement learning and imitation learning methods fail.

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ICML Conference 2023 Conference Paper

Simplified Temporal Consistency Reinforcement Learning

  • Yi Zhao 0014
  • Wenshuai Zhao
  • Rinu Boney
  • Juho Kannala
  • Joni Pajarinen

Reinforcement learning (RL) is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation. To improve sample efficiency, recent work focuses on model-based RL which interleaves model learning with planning. Recent methods further utilize policy learning, value estimation, and, self-supervised learning as auxiliary objectives. In this paper we show that, surprisingly, a simple representation learning approach relying only on a latent dynamics model trained by latent temporal consistency is sufficient for high-performance RL. This applies when using pure planning with a dynamics model conditioned on the representation, but, also when utilizing the representation as policy and value function features in model-free RL. In experiments, our approach learns an accurate dynamics model to solve challenging high-dimensional locomotion tasks with online planners while being 4. 1$\times$ faster to train compared to ensemble-based methods. With model-free RL without planning, especially on high-dimensional tasks, such as the Deepmind Control Suite Humanoid and Dog tasks, our approach outperforms model-free methods by a large margin and matches model-based methods’ sample efficiency while training 2. 4$\times$ faster.

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AAAI Conference 2023 Conference Paper

State-Conditioned Adversarial Subgoal Generation

  • Vivienne Huiling Wang
  • Joni Pajarinen
  • Tinghuai Wang
  • Joni-Kristian Kämäräinen

Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks by performing decision-making and control at successively higher levels of temporal abstraction. However, off-policy HRL often suffers from the problem of a non-stationary high-level policy since the low-level policy is constantly changing. In this paper, we propose a novel HRL approach for mitigating the non-stationarity by adversarially enforcing the high-level policy to generate subgoals compatible with the current instantiation of the low-level policy. In practice, the adversarial learning is implemented by training a simple state conditioned discriminator network concurrently with the high-level policy which determines the compatibility level of subgoals. Comparison to state-of-the-art algorithms shows that our approach improves both learning efficiency and performance in challenging continuous control tasks.

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ICLR Conference 2022 Conference Paper

Boosted Curriculum Reinforcement Learning

  • Pascal Klink
  • Carlo D'Eramo
  • Jan Peters 0001
  • Joni Pajarinen

Curriculum value-based reinforcement learning (RL) solves a complex target task by reusing action-values across a tailored sequence of related tasks of increasing difficulty. However, finding an exact way of reusing action-values in this setting is still a poorly understood problem. In this paper, we introduce the concept of boosting to curriculum value-based RL, by approximating the action-value function as a sum of residuals trained on each task. This approach, which we refer to as boosted curriculum reinforcement learning (BCRL), has the benefit of naturally increasing the representativeness of the functional space by adding a new residual each time a new task is presented. This procedure allows reusing previous action-values while promoting expressiveness of the action-value function. We theoretically study BCRL as an approximate value iteration algorithm, discussing advantages over regular curriculum RL in terms of approximation accuracy and convergence to the optimal action-value function. Finally, we provide detailed empirical evidence of the benefits of BCRL in problems requiring curricula for accurate action-value estimation and targeted exploration.

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EWRL Workshop 2022 Workshop Paper

Curriculum Reinforcement Learning via Constrained Optimal Transport

  • Pascal Klink
  • Haoyi Yang
  • Carlo D’Eramo
  • Jan Peters
  • Joni Pajarinen

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a tailored sequence of learning tasks, starting from easy ones and subsequently increasing their difficulty. Although the potential of curricula in RL has been clearly shown in a variety of works, it is less clear how to generate them for a given learning environment, resulting in a variety of methods aiming to automate this task. In this work, we focus on the idea of framing curricula as interpolations between task distributions, which has previously been shown to be a viable approach to CRL. Identifying key issues of existing methods, we frame the generation of a curriculum as a constrained optimal transport problem between task distributions. Benchmarks show that this way of curriculum generation can improve upon existing CRL methods, yielding high performance in a variety of tasks with different characteristics.

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ICML Conference 2022 Conference Paper

Curriculum Reinforcement Learning via Constrained Optimal Transport

  • Pascal Klink
  • Haoyi Yang
  • Carlo D'Eramo
  • Jan Peters 0001
  • Joni Pajarinen

Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a tailored sequence of learning tasks, starting from easy ones and subsequently increasing their difficulty. Although the potential of curricula in RL has been clearly shown in a variety of works, it is less clear how to generate them for a given learning environment, resulting in a variety of methods aiming to automate this task. In this work, we focus on the idea of framing curricula as interpolations between task distributions, which has previously been shown to be a viable approach to CRL. Identifying key issues of existing methods, we frame the generation of a curriculum as a constrained optimal transport problem between task distributions. Benchmarks show that this way of curriculum generation can improve upon existing CRL methods, yielding high performance in a variety of tasks with different characteristics.

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ICRA Conference 2022 Conference Paper

GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control

  • Abdolreza Taheri
  • Joni Pajarinen
  • Reza Ghabcheloo

The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robotics research. However, the computational complexity of GPs has made policy search a highly time and memory consuming process that has not been able to scale to larger problems. In this work, we develop a policy optimization method by leveraging fast predictive sampling methods to process batches of trajectories in every forward pass, and compute gradient updates over policy parameters by automatic differentiation of Monte Carlo evaluations, all on GPU. We demonstrate the effectiveness of our approach in training policies on a set of reference-tracking control experiments with a heavy-duty machine. Benchmark results show a significant speedup over exact methods and showcase the scalability of our method to larger policy networks, longer horizons, and up to thousands of trajectories with a sublinear drop in speed.

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NeurIPS Conference 2022 Conference Paper

Redeeming intrinsic rewards via constrained optimization

  • Eric Chen
  • Zhang-Wei Hong
  • Joni Pajarinen
  • Pulkit Agrawal

State-of-the-art reinforcement learning (RL) algorithms typically use random sampling (e. g. , $\epsilon$-greedy) for exploration, but this method fails on hard exploration tasks like Montezuma's Revenge. To address the challenge of exploration, prior works incentivize exploration by rewarding the agent when it visits novel states. Such intrinsic rewards (also called exploration bonus or curiosity) often lead to excellent performance on hard exploration tasks. However, on easy exploration tasks, the agent gets distracted by intrinsic rewards and performs unnecessary exploration even when sufficient task (also called extrinsic) reward is available. Consequently, such an overly curious agent performs worse than an agent trained with only task reward. Such inconsistency in performance across tasks prevents the widespread use of intrinsic rewards with RL algorithms. We propose a principled constrained optimization procedure called Extrinsic-Intrinsic Policy Optimization (EIPO) that automatically tunes the importance of the intrinsic reward: it suppresses the intrinsic reward when exploration is unnecessary and increases it when exploration is required. The results is superior exploration that does not require manual tuning in balancing the intrinsic reward against the task reward. Consistent performance gains across sixty-one ATARI games validate our claim. The code is available at https: //github. com/Improbable-AI/eipo.

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ICLR Conference 2022 Conference Paper

Topological Experience Replay

  • Zhang-Wei Hong
  • Tao Chen 0046
  • Yen-Chen Lin
  • Joni Pajarinen
  • Pulkit Agrawal 0001

State-of-the-art deep Q-learning methods update Q-values using state transition tuples sampled from the experience replay buffer. This strategy often randomly samples or prioritizes data sampling based on measures such as the temporal difference (TD) error. Such sampling strategies can be inefficient at learning Q-function since a state's correct Q-value preconditions on the accurate successor states' Q-value. Disregarding such a successor's value dependency leads to useless updates and even learning wrong values. To expedite Q-learning, we maintain states' dependency by organizing the agent's experience into a graph. Each edge in the graph represents a transition between two connected states. We perform value backups via a breadth-first search that expands vertices in the graph starting from the set of terminal states successively moving backward. We empirically show that our method is substantially more data-efficient than several baselines on a diverse range of goal-reaching tasks. Notably, the proposed method also outperforms baselines that consume more batches of training experience.

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JMLR Journal 2021 Journal Article

A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning

  • Pascal Klink
  • Hany Abdulsamad
  • Boris Belousov
  • Carlo D'Eramo
  • Jan Peters
  • Joni Pajarinen

Across machine learning, the use of curricula has shown strong empirical potential to improve learning from data by avoiding local optima of training objectives. For reinforcement learning (RL), curricula are especially interesting, as the underlying optimization has a strong tendency to get stuck in local optima due to the exploration-exploitation trade-off. Recently, a number of approaches for an automatic generation of curricula for RL have been shown to increase performance while requiring less expert knowledge compared to manually designed curricula. However, these approaches are seldomly investigated from a theoretical perspective, preventing a deeper understanding of their mechanics. In this paper, we present an approach for automated curriculum generation in RL with a clear theoretical underpinning. More precisely, we formalize the well-known self-paced learning paradigm as inducing a distribution over training tasks, which trades off between task complexity and the objective to match a desired task distribution. Experiments show that training on this induced distribution helps to avoid poor local optima across RL algorithms in different tasks with uninformative rewards and challenging exploration requirements. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

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ICML Conference 2021 Conference Paper

Convex Regularization in Monte-Carlo Tree Search

  • Tuan Dam
  • Carlo D'Eramo
  • Jan Peters 0001
  • Joni Pajarinen

Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. The recent AlphaGo and AlphaZero algorithms have shown how to successfully combine these two paradigms to solve large-scale sequential decision problems. These methodologies exploit a variant of the well-known UCT algorithm to trade off the exploitation of good actions and the exploration of unvisited states, but their empirical success comes at the cost of poor sample-efficiency and high computation time. In this paper, we overcome these limitations by introducing the use of convex regularization in Monte-Carlo Tree Search (MCTS) to drive exploration efficiently and to improve policy updates. First, we introduce a unifying theory on the use of generic convex regularizers in MCTS, deriving the first regret analysis of regularized MCTS and showing that it guarantees an exponential convergence rate. Second, we exploit our theoretical framework to introduce novel regularized backup operators for MCTS, based on the relative entropy of the policy update and, more importantly, on the Tsallis entropy of the policy, for which we prove superior theoretical guarantees. We empirically verify the consequence of our theoretical results on a toy problem. Finally, we show how our framework can easily be incorporated in AlphaGo and we empirically show the superiority of convex regularization, w. r. t. representative baselines, on well-known RL problems across several Atari games.

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ICRA Conference 2021 Conference Paper

Neural Network Controller for Autonomous Pile Loading Revised

  • Wenyan Yang
  • Nataliya Strokina
  • Nikolay Serbenyuk
  • Joni Pajarinen
  • Reza Ghabcheloo
  • Juho Vihonen
  • Mohammad M. Aref
  • Joni-Kristian Kämäräinen

We have recently proposed two pile loading controllers that learn from human demonstrations: a neural network (NNet) [1] and a random forest (RF) controller [2]. In the field experiments the RF controller obtained clearly better success rates. In this work, the previous findings are drastically revised by experimenting summer time trained controllers in winter conditions. The winter experiments revealed a need for additional sensors, more training data, and a controller that can take advantage of these. Therefore, we propose a revised neural controller (NNetV2) which has a more expressive structure and uses a neural attention mechanism to focus on important parts of the sensor and control signals. Using the same data and sensors to train and test the three controllers, NNetV2 achieves better robustness against drastically changing conditions and superior success rate. To the best of our knowledge, this is the first work testing a learning-based controller for a heavy-duty machine in drastically varying outdoor conditions and delivering high success rate in winter, being trained in summer.

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IROS Conference 2020 Conference Paper

Deep Adversarial Reinforcement Learning for Object Disentangling

  • Melvin Laux
  • Oleg Arenz
  • Jan Peters 0001
  • Joni Pajarinen

Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful robotic RL applications such as in-hand manipulation. However, most robotic RL relies on a well known initial state distribution. In real-world tasks, this information is however often not available. For example, when disentangling waste objects the actual position of the robot w. r. t. the objects may not match the positions the RL policy was trained for. To solve this problem, we present a novel adversarial reinforcement learning (ARL) framework. The ARL framework utilizes an adversary, which is trained to steer the original agent, the protagonist, to challenging states. We train the protagonist and the adversary jointly to allow them to adapt to the changing policy of their opponent. We show that our method can generalize from training to test scenarios by training an end-to-end system for robot control to solve a challenging object disentangling task. Experiments with a KUKA LBR+ 7-DOF robot arm show that our approach outperforms the baseline method in disentangling when starting from different initial states than provided during training.

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IJCAI Conference 2020 Conference Paper

Generalized Mean Estimation in Monte-Carlo Tree Search

  • Tuan Dam
  • Pascal Klink
  • Carlo D'Eramo
  • Jan Peters
  • Joni Pajarinen

We consider Monte-Carlo Tree Search (MCTS) applied to Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), and the well-known Upper Confidence bound for Trees (UCT) algorithm. In UCT, a tree with nodes (states) and edges (actions) is incrementally built by the expansion of nodes, and the values of nodes are updated through a backup strategy based on the average value of child nodes. However, it has been shown that with enough samples the maximum operator yields more accurate node value estimates than averaging. Instead of settling for one of these value estimates, we go a step further proposing a novel backup strategy which uses the power mean operator, which computes a value between the average and maximum value. We call our new approach Power-UCT, and argue how the use of the power mean operator helps to speed up the learning in MCTS. We theoretically analyze our method providing guarantees of convergence to the optimum. Finally, we empirically demonstrate the effectiveness of our method in well-known MDP and POMDP benchmarks, showing significant improvement in performance and convergence speed w. r. t. state of the art algorithms.

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JAAMAS Journal 2020 Journal Article

Multi-agent active information gathering in discrete and continuous-state decentralized POMDPs by policy graph improvement

  • Mikko Lauri
  • Joni Pajarinen
  • Jan Peters

Abstract Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest when constant communication cannot be assumed. This is common in tasks involving information gathering with multiple independently operating sensor devices that may operate over large physical distances, such as unmanned aerial vehicles, or in communication limited environments such as in the case of autonomous underwater vehicles. In this paper, we frame the information gathering task as a general decentralized partially observable Markov decision process (Dec-POMDP). The Dec-POMDP is a principled model for co-operative decentralized multi-agent decision-making. An optimal solution of a Dec-POMDP is a set of local policies, one for each agent, which maximizes the expected sum of rewards over time. In contrast to most prior work on Dec-POMDPs, we set the reward as a non-linear function of the agents’ state information, for example the negative Shannon entropy. We argue that such reward functions are well-suited for decentralized information gathering problems. We prove that if the reward function is convex, then the finite-horizon value function of the Dec-POMDP is also convex. We propose the first heuristic anytime algorithm for information gathering Dec-POMDPs, and empirically prove its effectiveness by solving discrete problems an order of magnitude larger than previous state-of-the-art. We also propose an extension to continuous-state problems with finite action and observation spaces by employing particle filtering. The effectiveness of the proposed algorithms is verified in domains such as decentralized target tracking, scientific survey planning, and signal source localization.

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NeurIPS Conference 2020 Conference Paper

Self-Paced Deep Reinforcement Learning

  • Pascal Klink
  • Carlo D'Eramo
  • Jan R. Peters
  • Joni Pajarinen

Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical successes, an open question in CRL is how to automatically generate a curriculum for a given reinforcement learning (RL) agent, avoiding manual design. In this paper, we propose an answer by interpreting the curriculum generation as an inference problem, where distributions over tasks are progressively learned to approach the target task. This approach leads to an automatic curriculum generation, whose pace is controlled by the agent, with solid theoretical motivation and easily integrated with deep RL algorithms. In the conducted experiments, the curricula generated with the proposed algorithm significantly improve learning performance across several environments and deep RL algorithms, matching or outperforming state-of-the-art existing CRL algorithms.

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AAMAS Conference 2019 Conference Paper

Information Gathering in Decentralized POMDPs by Policy Graph Improvement

  • Mikko Lauri
  • Joni Pajarinen
  • Jan Peters

Decentralized policies for information gathering are required when multiple autonomous agents are deployed to collect data about a phenomenon of interest without the ability to communicate. Decentralized partially observable Markov decision processes (Dec- POMDPs) are a general, principled model well-suited for such decentralized multiagent decision-making problems. In this paper, we investigate Dec-POMDPs for decentralized information gathering problems. An optimal solution of a Dec-POMDP maximizes the expected sum of rewards over time. To encourage information gathering, we set the reward as a function of the agents’ state information, for example the negative Shannon entropy. We prove that if the reward is convex, then the finite-horizon value function of the corresponding Dec-POMDP is also convex. We propose the first heuristic algorithm for information gathering Dec-POMDPs, and empirically prove its effectiveness by solving problems an order of magnitude larger than previous state-of-the-art.

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ICML Conference 2019 Conference Paper

Projections for Approximate Policy Iteration Algorithms

  • Riad Akrour
  • Joni Pajarinen
  • Jan Peters 0001
  • Gerhard Neumann

Approximate policy iteration is a class of reinforcement learning (RL) algorithms where the policy is encoded using a function approximator and which has been especially prominent in RL with continuous action spaces. In this class of RL algorithms, ensuring increase of the policy return during policy update often requires to constrain the change in action distribution. Several approximations exist in the literature to solve this constrained policy update problem. In this paper, we propose to improve over such solutions by introducing a set of projections that transform the constrained problem into an unconstrained one which is then solved by standard gradient descent. Using these projections, we empirically demonstrate that our approach can improve the policy update solution and the control over exploration of existing approximate policy iteration algorithms.

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IROS Conference 2017 Conference Paper

Hybrid control trajectory optimization under uncertainty

  • Joni Pajarinen
  • Ville Kyrki
  • Michael C. Koval
  • Siddhartha S. Srinivasa
  • Jan Peters 0001
  • Gerhard Neumann

Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i. e. hybrid controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable.

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AIJ Journal 2017 Journal Article

Robotic manipulation of multiple objects as a POMDP

  • Joni Pajarinen
  • Ville Kyrki

This paper investigates manipulation of multiple unknown objects in a crowded environment. Because of incomplete knowledge due to unknown objects and occlusions in visual observations, object observations are imperfect and action success is uncertain, making planning challenging. We model the problem as a partially observable Markov decision process (POMDP), which allows a general reward based optimization objective and takes uncertainty in temporal evolution and partial observations into account. In addition to occlusion dependent observation and action success probabilities, our POMDP model also automatically adapts object specific action success probabilities. To cope with the changing system dynamics and performance constraints, we present a new online POMDP method based on particle filtering that produces compact policies. The approach is validated both in simulation and in physical experiments in a scenario of moving dirty dishes into a dishwasher. The results indicate that: 1) a greedy heuristic manipulation approach is not sufficient, multi-object manipulation requires multi-step POMDP planning, and 2) on-line planning is beneficial since it allows the adaptation of the system dynamics model based on actual experience.

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IROS Conference 2016 Conference Paper

Learning in-contact control strategies from demonstration

  • Mattia Racca
  • Joni Pajarinen
  • Alberto Montebelli
  • Ville Kyrki

Learning to perform tasks like pulling a door handle or pushing a button, inherently easy for a human, can be surprisingly difficult for a robot. A crucial problem in these kinds of in-contact tasks is the context specificity of pose and force requirements. In this paper, a robot learns in-contact tasks from human kinesthetic demonstrations. To address the need to balance between the position and force constraints, we propose a model based on the hidden semi-Markov model (HSMM) and Cartesian impedance control. The model captures uncertainty over time and space and allows the robot to smoothly satisfy a task's position and force constraints by online modulation of impedance controller stiffness according to the HSMM state belief. In experiments, a KUKA LWR 4+ robotic arm equipped with a force/torque sensor at the wrist successfully learns from human demonstrations how to pull a door handle and push a button.

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AAAI Conference 2016 Conference Paper

Sparse Latent Space Policy Search

  • Kevin Luck
  • Joni Pajarinen
  • Erik Berger
  • Ville Kyrki
  • Heni Ben Amor

Computational agents often need to learn policies that involve many control variables, e. g. , a robot needs to control several joints simultaneously. Learning a policy with a high number of parameters, however, usually requires a large number of training samples. We introduce a reinforcement learning method for sampleefficient policy search that exploits correlations between control variables. Such correlations are particularly frequent in motor skill learning tasks. The introduced method uses Variational Inference to estimate policy parameters, while at the same time uncovering a lowdimensional latent space of controls. Prior knowledge about the task and the structure of the learning agent can be provided by specifying groups of potentially correlated parameters. This information is then used to impose sparsity constraints on the mapping between the high-dimensional space of controls and a lowerdimensional latent space. In experiments with a simulated bi-manual manipulator, the new approach effectively identifies synergies between joints, performs ef- ficient low-dimensional policy search, and outperforms state-of-the-art policy search methods.

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ICRA Conference 2015 Conference Paper

Decision making under uncertain segmentations

  • Joni Pajarinen
  • Ville Kyrki

Making decisions based on visual input is challenging because determining how the scene should be split into individual objects is often very difficult. While previous work mainly considers decision making and visual processing as two separate tasks, we argue that the inherent uncertainty in object segmentation requires an integrated approach that chooses the best decision over all possible segmentations. Our approach over-segments the visual input and combines the segments into possible objects to get a probability distribution over object compositions, represented as particles. We introduce a Markov chain Monte Carlo procedure that aims to produce exact, independent samples. In experiments, where a 6-DOF robot arm moves object hypotheses captured by an RGB-D visual sensor, our approach of probability distribution based decision making outperforms an approach which utilises the traditional most likely object composition.

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IROS Conference 2014 Conference Paper

Real-time recognition of pointing gestures for robot to robot interaction

  • Polychronis Kondaxakis
  • Joni Pajarinen
  • Ville Kyrki

This paper addresses the idea of establishing symbolic communication between mobile robots through gesturing. Humans communicate using body language and gestures in addition to other linguistic modalities like prosody and text or dialog structure. This research aims to develop a pointing gesture detection system for robot to robot communication scenarios to grant robots an ability to convey object identity information without global localization of the agents. The detection is based on RGB-D and a NAO humanoid robot is used as the pointing agent in the experiments. The presented algorithms are based on PCL library. The results indicate that real-time detection of pointing gesture can be performed with little information about the embodiment of the pointing agent and that an observing agent can use the gesture detection to perform actions on the pointed targets.

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IROS Conference 2014 Conference Paper

Robotic manipulation in object composition space

  • Joni Pajarinen
  • Ville Kyrki

Manipulating unknown objects in a cluttered environment is difficult because object composition is uncertain. Because of this uncertainty, earlier work has concentrated on finding the “best” object composition and based on this composition decided on manipulation actions. Contrary to earlier work, we 1) utilize different possible object compositions in decision making, 2) take advantage of object composition information provided by robot actions, 3) take into account the effect of different competing object hypothesis on the actual task to be performed. We cast the manipulation planning problem as a partially observable Markov decision process (POMDP) which plans over possible hypotheses of object compositions. The POMDP model chooses the action that maximizes the long-term expected task specific utility, and while doing so, considers the value of informative actions and the effect of different object hypotheses on the completion of the task. In experiments with a physical robot arm and an RGB-D sensor, our approach outperforms an approach that only considers the most likely object composition.

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IJCAI Conference 2011 Conference Paper

Efficient Planning for Factored Infinite-Horizon DEC-POMDPs

  • Joni Pajarinen
  • Jaakko Peltonen

Decentralized partially observable Markov decision processes (DEC-POMDPs) are used to plan policies for multiple agents that must maximize a joint reward function but do not communicate with each other. The agents act under uncertainty about each other and the environment. This planning task arises in optimization of wireless networks, and other scenarios where communication between agents is restricted by costs or physical limits. DEC-POMDPs are a promising solution, but optimizing policies quickly becomes computationally intractable when problem size grows. Factored DEC-POMDPs allow large problems to be described in compact form, but have the same worst case complexity as non-factored DEC-POMDPs. We propose an efficient optimization algorithm for large factored infinite-horizon DEC-POMDPs. We formulate expectation-maximization based optimization into a new form, where complexity can be kept tractable by factored approximations. Our method performs well, and it can solve problems with more agents and larger state spaces than state of the art DEC-POMDP methods. We give results for factored infinite-horizon DEC-POMDP problems with up to 10 agents.

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NeurIPS Conference 2011 Conference Paper

Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning

  • Joni Pajarinen
  • Jaakko Peltonen

Applications such as robot control and wireless communication require planning under uncertainty. Partially observable Markov decision processes (POMDPs) plan policies for single agents under uncertainty and their decentralized versions (DEC-POMDPs) find a policy for multiple agents. The policy in infinite-horizon POMDP and DEC-POMDP problems has been represented as finite state controllers (FSCs). We introduce a novel class of periodic FSCs, composed of layers connected only to the previous and next layer. Our periodic FSC method finds a deterministic finite-horizon policy and converts it to an initial periodic infinite-horizon policy. This policy is optimized by a new infinite-horizon algorithm to yield deterministic periodic policies, and by a new expectation maximization algorithm to yield stochastic periodic policies. Our method yields better results than earlier planning methods and can compute larger solutions than with regular FSCs.

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