Author name cluster

Roland Hafner

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12 papers

2 author rows

ICRA Conference 2025 Conference Paper

DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots

Maria Bauzá 0001
Jose Enriaue Chen
Valentin Dalibard
Nimrod Gileadi
Roland Hafner
Murilo F. Martins
Joss Moore
Rugile Pevceviciute

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three- fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces the development cycle of behavior generation, and domain randomization techniques are leveraged to achieve successful zero-shot sim-to- real transfer. Transferred policies are learned directly from raw pixels from multiple cameras and robot proprioception. Our approach outperforms policies learned from demonstrations on the real robot and requires 100 times fewer demonstrations, collected in simulation. More details and videos in sites.google.com/view/demostart.

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ICRA Conference 2024 Conference Paper

Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots

Thomas Lampe
Abbas Abdolmaleki
Sarah Bechtle
Sandy Han Huang
Jost Tobias Springenberg
Michael Bloesch
Oliver Groth
Roland Hafner

Reinforcement learning solely from an agent’s self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed. However, if done right, agents learning from real data can be surprisingly efficient through re-using previously collected sub-optimal data. In this paper we demonstrate how the increased understanding of off-policy learning methods and their embedding in an iterative online/offline scheme ("collect and infer") can drastically improve data-efficiency by using all the collected experience, which empowers learning from real robot experience only. Moreover, the resulting policy improves significantly over the state of the art on a recently proposed real robot manipulation benchmark. Our approach learns end-to-end, directly from pixels, and does not rely on additional human domain knowledge such as a simulator or demonstrations.

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ICML Conference 2024 Conference Paper

Offline Actor-Critic Reinforcement Learning Scales to Large Models

Jost Tobias Springenberg
Abbas Abdolmaleki
Jingwei Zhang 0001
Oliver Groth
Michael Bloesch
Thomas Lampe
Philemon Brakel
Sarah Bechtle

We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that offline actor-critic algorithms can outperform strong, supervised, behavioral cloning baselines for multi-task training on a large dataset; containing both sub-optimal and expert behavior on 132 continuous control tasks. We introduce a Perceiver-based actor-critic model and elucidate the key features needed to make offline RL work with self- and cross-attention modules. Overall, we find that: i) simple offline actor critic algorithms are a natural choice for gradually moving away from the currently predominant paradigm of behavioral cloning, and ii) via offline RL it is possible to learn multi-task policies that master many domains simultaneously, including real robotics tasks, from sub-optimal demonstrations or self-generated data.

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IROS Conference 2024 Conference Paper

The Design of the Barkour Benchmark for Robot Agility

Wenhao Yu 0003
Ken Caluwaerts
Atil Iscen
J. Chase Kew
Tingnan Zhang
Daniel Freeman
Lisa Lee
Stefano Saliceti

In this paper, we describe the design of the Barkour benchmark for measuring robot agility in navigating complex environments. Despite the growing interest in developing agile robot locomotion skills, the field lacks systematic benchmarks to measure the performance of robotic control systems and hardware in agility-focused tasks. This motivated us to propose the Barkour benchmark, an obstacle course designed to quantify agility across various robotic platforms. Inspired by dog agility competitions, the course features diverse obstacles and a time-based scoring mechanism, encouraging researchers to develop controllers that enable robots to move quickly, precisely, and with adaptability. This benchmark is challenging as it demands diverse motion skills and the time-based scoring requires control precision at high speed. Along with the design details presented in the paper, we release our simulated environment setups in MuJoCo-XLA and the CAD model of a custom-designed quadruped robot to facilitate future research to reproduce the Barkour setup (available at sites.google.com/view/barkour). We hope these together will accelerate the pace of robot agility research.

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TMLR Journal 2023 Journal Article

SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration

Giulia Vezzani
Dhruva Tirumala
Markus Wulfmeier
Dushyant Rao
Abbas Abdolmaleki
Ben Moran
Tuomas Haarnoja
Jan Humplik

The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations. For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution. It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of different components of our method.

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ICML Conference 2021 Conference Paper

Data-efficient Hindsight Off-policy Option Learning

Markus Wulfmeier
Dushyant Rao
Roland Hafner
Thomas Lampe
Abbas Abdolmaleki
Tim Hertweck
Michael Neunert
Dhruva Tirumala

We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm. Given any trajectory, HO2 infers likely option choices and backpropagates through the dynamic programming inference procedure to robustly train all policy components off-policy and end-to-end. The approach outperforms existing option learning methods on common benchmarks. To better understand the option framework and disentangle benefits from both temporal and action abstraction, we evaluate ablations with flat policies and mixture policies with comparable optimization. The results highlight the importance of both types of abstraction as well as off-policy training and trust-region constraints, particularly in challenging, simulated 3D robot manipulation tasks from raw pixel inputs. Finally, we intuitively adapt the inference step to investigate the effect of increased temporal abstraction on training with pre-trained options and from scratch.

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ICRA Conference 2021 Conference Paper

Representation Matters: Improving Perception and Exploration for Robotics

Markus Wulfmeier
Arunkumar Byravan
Tim Hertweck
Irina Higgins
Ankush Gupta
Tejas Kulkarni
Malcolm Reynolds
Denis Teplyashin

Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domains with limited data such as robotics. Can a single generally useful representation be found? In order to answer this question, it is important to understand how the representation will be used by the agent and what properties such a good representation should have. In this paper we systematically evaluate a number of common learnt and hand-engineered representations in the context of three robotics tasks: lifting, stacking and pushing of 3D blocks. The representations are evaluated in two use-cases: as input to the agent, or as a source of auxiliary tasks. Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement. We can significantly improve performance in both use-cases and demonstrate that some representations can perform commensurate to simulator states as agent inputs. Finally, our results challenge common intuitions by demonstrating that: 1) dimensionality strongly matters for task generation, but is negligible for inputs, 2) observability of task-relevant aspects mostly affects the input representation use-case, and 3) disentanglement leads to better auxiliary tasks, but has only limited benefits for input representations. This work serves as a step towards a more systematic understanding of what makes a good representation for control in robotics, enabling practitioners to make more informed choices for developing new learned or hand-engineered representations.

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ICLR Conference 2020 Conference Paper

Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning

Noah Y. Siegel
Jost Tobias Springenberg
Felix Berkenkamp
Abbas Abdolmaleki
Michael Neunert
Thomas Lampe
Roland Hafner
Nicolas Heess

Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired. This property makes these algorithms appealing for real world problems such as robot control. In practice, however, standard off-policy algorithms fail in the batch setting for continuous control. In this paper, we propose a simple solution to this problem. It admits the use of data generated by arbitrary behavior policies and uses a learned prior -- the advantage-weighted behavior model (ABM) -- to bias the RL policy towards actions that have previously been executed and are likely to be successful on the new task. Our method can be seen as an extension of recent work on batch-RL that enables stable learning from conflicting data-sources. We find improvements on competitive baselines in a variety of RL tasks -- including standard continuous control benchmarks and multi-task learning for simulated and real-world robots.

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ICML Conference 2018 Conference Paper

Learning by Playing Solving Sparse Reward Tasks from Scratch

Martin A. Riedmiller
Roland Hafner
Thomas Lampe
Michael Neunert
Jonas Degrave
Tom Van de Wiele
Volodymyr Mnih
Nicolas Heess

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch - in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary tasks, that it attempts to learn simultaneously via off-policy RL. The key idea behind our method is that active (learned) scheduling and execution of auxiliary policies allows the agent to efficiently explore its environment - enabling it to excel at sparse reward RL. Our experiments in several challenging robotic manipulation settings demonstrate the power of our approach.

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ICRA Conference 2008 Conference Paper

Learning to dribble on a real robot by success and failure

Martin A. Riedmiller
Roland Hafner
Sascha Lange
Martin Lauer

Learning directly on real world systems such as autonomous robots is a challenging task, especially if the training signal is given only in terms of success or failure (Reinforcement Learning). However, if successful, the controller has the advantage of being tailored exactly to the system it eventually has to control. Here we describe, how a neural network based RL controller learns the challenging task of ball dribbling directly on our Middle-Size robot. The learned behaviour was actively used throughout the RoboCup world championship tournament 2007 in Atlanta, where we won the first place. This contistutes another important step within our Brainstormers project. The goal of this project is to develop an intelligent control architecture for a soccer playing robot, that is able to learn more and more complex behaviours from scratch.

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ICRA Conference 2007 Conference Paper

Neural Reinforcement Learning Controllers for a Real Robot Application

Roland Hafner
Martin A. Riedmiller

Accurate and fast control of wheel speeds in the presence of noise and nonlinearities is one of the crucial requirements for building fast mobile robots, as they are required in the MiddleSize League of RoboCup. We will describe, how highly effective speed controllers can be learned from scratch on the real robot directly. The use of our recently developed neural fitted Q iteration scheme allows reinforcement learning of neural controllers with only a limited amount of training data seen. In the described application, less than 5 minutes of interaction with the real robot were sufficient, to learn fast and accurate control to arbitrary target speeds.

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IROS Conference 2003 Conference Paper

Reinforcement learning on an omnidirectional mobile robot

Roland Hafner
Martin A. Riedmiller

With this paper we describe a well suited, scalable problem for reinforcement learning approaches in the field of mobile robots. We show a suitable representation of the problem for a reinforcement approach and present our results with a model based standard algorithm. Two different approximators for the value function are used, a grid based approximator and a neural network based approximator.

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