Carme Torras

ICRA Conference 2025 Conference Paper

BiFold: Bimanual Cloth Folding with Language Guidance

• Oriol Barbany
• Adrià Colomé
• Carme Torras

Cloth folding is a complex task due to the inevitable self-occlusions of clothes, their complicated dynamics, and the disparate materials, geometries, and textures that garments can have. In this work, we learn folding actions conditioned on text commands. Translating high-level, abstract instructions into precise robotic actions requires sophisticated language understanding and manipulation capabilities. To do that, we leverage a pre-trained vision-language model and repurpose it to predict manipulation actions. Our model, BiFold, can take context into account and achieves state-of-the-art performance on an existing language-conditioned folding benchmark. To address the lack of annotated bimanual folding data, we introduce a novel dataset with automatically parsed actions and language-aligned instructions, enabling better learning of text-conditioned manipulation. BiFold attains the best performance on our dataset and demonstrates strong generalization to new instructions, garments, and environments.

IROS Conference 2025 Conference Paper

Evaluating the Pre-Dressing Step: Unfolding Medical Garments via Imitation Learning

• David Blanco-Mulero
• Júlia Borràs 0001
• Carme Torras

Robotic-assisted dressing has the potential to significantly aid both patients as well as healthcare personnel, reducing the workload and improving the efficiency in clinical settings. While substantial progress has been made in robotic dressing assistance, prior works typically assume that garments are already unfolded and ready for use. However, in medical applications gowns and aprons are often stored in a folded configuration, requiring an additional unfolding step. In this paper, we introduce the pre-dressing step, the process of unfolding garments prior to assisted dressing. We leverage imitation learning for learning three manipulation primitives, including both high and low acceleration motions. In addition, we employ a visual classifier to categorise the garment state as closed, partly opened, and fully opened. We conduct an empirical evaluation of the learned manipulation primitives as well as their combinations. Our results show that highly dynamic motions are not effective for unfolding freshly unpacked garments, where the combination of motions can efficiently enhance the opening configuration.

ICRA Conference 2024 Conference Paper

Fitting Parameters of Linear Dynamical Systems to Regularize Forcing Terms in Dynamical Movement Primitives

• Freek Stulp
• Adrià Colomé
• Carme Torras

Due to their flexibility and ease of use, Dynamical Movement Primitives (DMPs) are widely used in robotics applications and research. DMPs combine linear dynamical systems to achieve robustness to perturbations and adaptation to moving targets with non-linear function approximators to fit a wide range of demonstrated trajectories. We propose a novel DMP formulation with a generalized logistic function as a delayed goal system. This formulation inherently has low initial jerk, and generates the bell-shaped velocity profiles that are typical of human movement. As the novel formulation is more expressive, it is able to fit a wide range of human demonstrations well, also without a non-linear forcing term. We exploit this increased expressiveness by automating the fitting of the dynamical system parameters through opti-mization. Our experimental evaluation demonstrates that this optimization regularizes the forcing term, and improves the interpolation accuracy of parametric DMPs.

IROS Conference 2024 Conference Paper

Zero-Shot Transfer of a Tactile-based Continuous Force Control Policy from Simulation to Robot

• Luca Lach
• Robert Haschke
• Davide Tateo
• Jan Peters 0001
• Helge J. Ritter
• Júlia Borràs 0001
• Carme Torras

The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks. An important line of research in this regard is grasp force control, which aims to manipulate objects safely by limiting the amount of force exerted on the object. While prior works have either hand-modeled their force controllers, employed model-based approaches, or not shown sim-to-real transfer, we propose a model-free deep reinforcement learning approach trained in simulation and then transferred to the robot without further fine-tuning. We, therefore, present a simulation environment that produces realistic normal forces, which we use to train continuous force control policies. A detailed evaluation shows that the learned policy performs similarly or better than a hand-crafted baseline. Ablation studies prove that the proposed inductive bias and domain randomization facilitate sim-to-real transfer. Code, models, and supplementary videos are available on https://sites.google.com/view/rl-force-ctrl

ICRA Conference 2023 Conference Paper

A Virtual Reality Framework For Fast Dataset Creation Applied to Cloth Manipulation with Automatic Semantic Labelling

• Júlia Borràs 0001
• Arnau Boix-Granell
• Sergi Foix
• Carme Torras

Teaching complex manipulation skills, such as folding garments, to a bi-manual robot is a very challenging task, which is often tackled through learning from demon-stration. The few datasets of garment-folding demonstrations available nowadays to the robotics research community have been either gathered from human demonstrations or generated through simulation. The former have the great difficulty of perceiving both cloth state and human action as well as transferring them to the dynamic control of the robot, while the latter require coding human motion into the simulator in open loop, i. e. , without incorporating the visual feedback naturally used by people, resulting in far-from-realistic movements. In this article, we present an accurate dataset of human cloth folding demonstrations. The dataset is collected through our novel virtual reality (VR) framework, based on Unity's 3D platform and the use of an HTC Vive Pro system. The framework is capable of simulating realistic garments while allowing users to interact with them in real time through handheld controllers. By doing so, and thanks to the immersive experience, our framework permits exploiting human visual feedback in the demonstrations while at the same time getting rid of the difficulties of capturing the state of cloth, thus simplifying data acquisition and resulting in more realistic demonstrations. We create and make public a dataset of cloth manipulation sequences, whose cloth states are semantically labeled in an automatic way by using a novel low-dimensional cloth representation that yields a very good separation between different cloth configurations.

IROS Conference 2023 Conference Paper

Quadratic Dynamic Matrix Control for Fast Cloth Manipulation

• Edoardo Caldarelli
• Adrià Colomé
• Carlos Ocampo-Martinez
• Carme Torras

Robotic cloth manipulation is an increasingly relevant area of research, challenging classic control algorithms due to the deformable nature of cloth. While it is possible to apply linear model predictive control to make the robot move the cloth according to a given reference, this approach suffers from a large dimensionality of the state-space representation of the cloth models. To address this issue, in this work we study the application of an input-output model predictive control strategy, based on quadratic dynamic matrix control, to robotic cloth manipulation. To account for uncertain disturbances on the cloth's motion, we further extend the algorithm with suitable chance constraints. In extensive simulated experiments, involving disturbances and obstacle avoidance, we show that quadratic dynamic matrix control can be successfully applied in different cloth manipulation scenarios, with significant gains in optimization speed compared to standard model predictive control strategies. The experiments further demonstrate that the closed-loop model used by quadratic dynamic matrix control can be beneficial to the tracking accuracy, leading to improvements over the standard predictive control strategy. Moreover, a preliminary experiment on a real robot shows that quadratic dynamic matrix control can indeed be employed in real settings.

IROS Conference 2023 Conference Paper

User Interactions and Negative Examples to Improve the Learning of Semantic Rules in a Cognitive Exercise Scenario

• Alejandro Suárez-Hernández
• Antonio Andriella
• Carme Torras
• Guillem Alenyà

Enabling a robot to perform new tasks is a complex endeavor, usually beyond the reach of non-technical users. For this reason, research efforts that aim at empowering end-users to teach robots new abilities using intuitive modes of interaction are valuable. In this article, we present INtuitive PROgramming 2 (INPRO2), a learning framework that allows inferring planning actions from demonstrations given by a human teacher. INPRO2 operates in an assistive scenario, in which the robot may learn from a healthcare professional (a therapist or caregiver) new cognitive exercises that can be later administered to patients with cognitive impairment. INPRO2 features significant improvements over previous work, namely: (1) exploitation of negative examples; (2) proactive interaction with the teacher to ask questions about the legality of certain movements; and (3) learning goals in addition to legal actions. Through simulations, we show the performance of different proactive strategies for gathering negative examples. Real-world experiments with human teachers and a TIAGo robot are also presented to qualitatively illustrate INPRO2.

IROS Conference 2022 Conference Paper

Ordinal Inverse Reinforcement Learning Applied to Robot Learning with Small Data

• Adrià Colomé
• Carme Torras

Over the last decade, the ability to teach actions to robots in a user-friendly way has gained relevance, and a practical way of teaching robots a new task is to use Inverse Reinforcement Learning (IRL). In IRL, an expert teacher shows the robot a desired behaviour and an agent builds a model of the reward. The agent can also infer a policy that performs in an optimal way within the limitations of the knowledge provided to it. However, most IRL approaches assume an (almost) optimal performance of the teaching agent, which might become unpractical if the teacher is not actually an expert. In addition, most IRL focus on discrete state-action spaces that limit their applicability to certain real-world problems such as within the context of direct Policy Search (PS) reinforcement learning. Therefore, in this paper we introduce Ordinal Inverse Reinforcement Learning (OrdIRL) for continuous state variables, in which the teacher can qualitatively evaluate robot performance by selecting one among the predefined performance levels (e. g. {bad, medium, good} for three tiers of performance). Once the OrdIRL has fit an ordinal distribution to the data, we propose to use Bayesian Optimization (BO) to either gain knowledge on the inferred model (exploration) or find a policy or action that maximizes the expected reward given the prior knowledge on the reward (exploitation). In the case of large-dimensional state-action spaces, we use Dimensionality Reduction (DR) techniques and perform the BO in the latent space. Experimental results on simulation and with a robot arm show how this approach allows for learning the reward function with small data.

AAAI Conference 2021 Conference Paper

Online Action Recognition

• Alejandro Suárez-Hernández
• Javier Segovia-Aguas
• Carme Torras
• Guillem Alenyà

Recognition in planning seeks to find agent intentions, goals or activities given a set of observations and a knowledge library (e. g. goal states, plans or domain theories). In this work we introduce the problem of Online Action Recognition. It consists in recognizing, in an open world, the planning action that best explains a partially observable state transition from a knowledge library of first-order STRIPS actions, which is initially empty. We frame this as an optimization problem, and propose two algorithms to address it: Action Unification (AU) and Online Action Recognition through Unification (OARU). The former builds on logic unification and generalizes two input actions using weighted partial MaxSAT. The latter looks for an action within the library that explains an observed transition. If there is such action, it generalizes it making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts a Trivial Grounded Action (TGA) in the library that explains just that transition. We report results on benchmarks from the International Planning Competition and PDDLGym, where OARU recognizes actions accurately with respect to expert knowledge, and shows real-time performance.

IROS Conference 2020 Conference Paper

Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models

• Juan Antonio Delgado-Guerrero
• Adrià Colomé
• Carme Torras

In the next few years, the amount and variety of context-aware robotic manipulator applications is expected to increase significantly, especially in household environments. In such spaces, thanks to programming by demonstration, non-expert people will be able to teach robots how to perform specific tasks, for which the adaptation to the environment is imperative, for the sake of effectiveness and users safety. These robot motion learning procedures allow the encoding of such tasks by means of parameterized trajectory generators, usually a Movement Primitive (MP) conditioned on contextual variables. However, naively sampled solutions from these MPs are generally suboptimal/inefficient, according to a given reward function. Hence, Policy Search (PS) algorithms leverage the information of the experienced rewards to improve the robot performance over executions, even for new context configurations. Given the complexity of the aforementioned tasks, PS methods face the challenge of exploring in high-dimensional parameter search spaces. In this work, a solution combining Bayesian Optimization, a data-efficient PS algorithm, with covariate Gaussian Process Latent Variable Models, a recent Dimensionality Reduction technique, is presented. It enables reducing dimensionality and exploiting prior demonstrations to converge in few iterations, while also being compliant with context requirements. Thus, contextual variables are considered in the latent search space, from which a surrogate model for the reward function is built. Then, samples are generated in a low-dimensional latent space, and mapped to a context-dependent trajectory. This allows us to drastically reduce the search space with the covariate GPLVM, e. g. from 105 to 2 parameters, plus a few contextual features. Experimentation in two different scenarios proves the data-efficiency and the power of dimensionality reduction of our approach.

ICRA Conference 2020 Conference Paper

Dynamic Cloth Manipulation with Deep Reinforcement Learning

• Rishabh Jangir
• Guillem Alenyà
• Carme Torras

In this paper we present a Deep Reinforcement Learning approach to solve dynamic cloth manipulation tasks. Differing from the case of rigid objects, we stress that the followed trajectory (including speed and acceleration) has a decisive influence on the final state of cloth, which can greatly vary even if the positions reached by the grasped points are the same. We explore how goal positions for non-grasped points can be attained through learning adequate trajectories for the grasped points. Our approach uses few demonstrations to improve control policy learning, and a sparse reward approach to avoid engineering complex reward functions. Since perception of textiles is challenging, we also study different state representations to assess the minimum observation space required for learning to succeed. Finally, we compare different combinations of control policy encodings, demonstrations, and sparse reward learning techniques, and show that our proposed approach can learn dynamic cloth manipulation in an efficient way, i. e. , using a reduced observation space, a few demonstrations, and a sparse reward.

IROS Conference 2020 Conference Paper

Leveraging Multiple Environments for Learning and Decision Making: a Dismantling Use Case

• Alejandro Suárez-Hernández
• Thierry Gaugry
• Javier Segovia-Aguas
• Antonin Bernardin
• Carme Torras
• Maud Marchal
• Guillem Alenyà

Learning is usually performed by observing real robot executions. Physics-based simulators are a good alternative for providing highly valuable information while avoiding costly and potentially destructive robot executions. We present a novel approach for learning the probabilities of symbolic robot action outcomes. This is done leveraging different environments, such as physics-based simulators, in execution time. To this end, we propose MENID (Multiple Environment Noise Indeterministic Deictic) rules, a novel representation able to cope with the inherent uncertainties present in robotic tasks. MENID rules explicitly represent each possible outcomes of an action, keep memory of the source of the experience, and maintain the probability of success of each outcome. We also introduce an algorithm to distribute actions among environments, based on previous experiences and expected gain. Before using physics-based simulations, we propose a methodology for evaluating different simulation settings and determining the least time-consuming model that could be used while still producing coherent results. We demonstrate the validity of the approach in a dismantling use case, using a simulation with reduced quality as simulated system, and a simulation with full resolution where we add noise to the trajectories and some physical parameters as a representation of the real system.

ICRA Conference 2020 Conference Paper

Sample-Efficient Robot Motion Learning using Gaussian Process Latent Variable Models

• Juan Antonio Delgado-Guerrero
• Adrià Colomé
• Carme Torras

Robotic manipulators are reaching a state where we could see them in household environments in the following decade. Nevertheless, such robots need to be easy to instruct by lay people. This is why kinesthetic teaching has become very popular in recent years, in which the robot is taught a motion that is encoded as a parametric function - usually a Movement Primitive (MP)-. This approach produces trajectories that are usually suboptimal, and the robot needs to be able to improve them through trial-and-error. Such optimization is often done with Policy Search (PS) reinforcement learning, using a given reward function. PS algorithms can be classified as model-free, where neither the environment nor the reward function are modelled, or model-based, which can use a surrogate model of the reward function and/or a model for the dynamics of the task. However, MPs can become very high-dimensional in terms of parameters, which constitute the search space, so their optimization often requires too many samples. In this paper, we assume we have a robot motion task characterized with an MP of which we cannot model the dynamics. We build a surrogate model for the reward function, that maps an MP parameter latent space (obtained through a Mutual-information-weighted Gaussian Process Latent Variable Model) into a reward. While we do not model the task dynamics, using mutual information to shrink the task space makes it more consistent with the reward and so the policy improvement is faster in terms of sample efficiency.

ICRA Conference 2020 Conference Paper

Variable Impedance Control in Cartesian Latent Space while Avoiding Obstacles in Null Space

• David Parent
• Adrià Colomé
• Carme Torras

Human-robot interaction is one of the keys of assistive robots. Robots are expected to be compliant with people but at the same time correctly perform the tasks. In such applications, Cartesian impedance control is preferred over joint control, as the desired interaction and environmental feedback can be described more naturally, and the force to be exerted by the robot can be readily adjusted. This paper addresses the problem of controlling a robot arm in the operational space with variable stiffness so as to continuously adapt the force exerted in each phase of motion according to the precision requirements. Moreover, performing dimensionality reduction we can separate the degrees of freedom (DoF) relevant for the task from the redundant ones. The stiffness of the former can be adjusted constantly to achieve the required accuracy, while task-redundant DoF can be used to achieve other goals such as avoiding obstacles by moving in the directions where accuracy is not critical. The designed method is tested teaching the robot to give water to drink to a model of human head. Our empirical results demonstrate that the robot can learn precision requirements from demonstration. Furthermore, dimensionality reduction is proved to be useful to avoid obstacles.

IROS Conference 2018 Conference Paper

Adaptive Modality Selection Algorithm in Robot-Assisted Cognitive Training

• Aleksandar Taranovic
• Aleksandar Jevtic
• Carme Torras

Interaction of socially assistive robots with users is based on social cues coming from different interaction modalities, such as speech or gestures. However, using all modalities at all times may be inefficient as it can overload the user with redundant information and increase the task completion time. Additionally, users may favor certain modalities over the other as a result of their disability or personal preference. In this paper, we propose an Adaptive Modality Selection (AMS) algorithm that chooses modalities depending on the state of the user and the environment, as well as user preferences. The variables that describe the environment and the user state are defined as resources, and we posit that modalities are successful if certain resources possess specific values during their use. Besides the resources, the proposed algorithm takes into account user preferences which it learns while interacting with users. We tested our algorithm in simulations, and we implemented it on a robotic system that provides cognitive training, specifically Sequential memory exercises. Experimental results show that it is possible to use only a subset of available modalities without compromising the interaction. Moreover, we see a trend for users to perform better when interacting with a system with implemented AMS algorithm.

IROS Conference 2018 Conference Paper

Interleaving Hierarchical Task Planning and Motion Constraint Testing for Dual-Arm Manipulation

• Alejandro Suárez-Hernández
• Guillem Alenyà
• Carme Torras

In recent years the topic of combining motion and symbolic planning to perform complex tasks in the field of robotics has received a lot of attention. The underlying idea is to have access at once to the reasoning capabilities of a task planner and to the ability of the motion planner to verify that the plan is feasible from a physical and geometrical point of view. The present work describes a framework to perform manipulation tasks that require the use of two robotic manipulators. To do so we employ a Hierarchical Task Network (HTN) planner interleaved with geometric constraint verification. In this framework we also consider observation actions and handle noisy perceptions from a probabilistic perspective. These ideas are put into practice by means of an experimental set-up in which two Barrett WAM robots have to cooperatively solve a geometric puzzle. Our findings provide further evidence that considering explicitly physical constraints during task planning, rather than deferring their validation to the moment of execution, is advantageous in terms of execution time and breadth of situations that can be handled.

ICRA Conference 2018 Conference Paper

Joining High-Level Symbolic Planning with Low-Level Motion Primitives in Adaptive HRI: Application to Dressing Assistance

• Gerard Canal
• Emmanuel Pignat
• Guillem Alenyà
• Sylvain Calinon
• Carme Torras

For a safe and successful daily living assistance, far from the highly controlled environment of a factory, robots should be able to adapt to ever-changing situations. Programming such a robot is a tedious process that requires expert knowledge. An alternative is to rely on a high-level planner, but the generic symbolic representations used are not well suited to particular robot executions. Contrarily, motion primitives encode robot motions in a way that can be easily adapted to different situations. This paper presents a combined framework that exploits the advantages of both approaches. The number of required symbolic states is reduced, as motion primitives provide “smart actions” that take the current state and cope online with variations. Symbolic actions can include interactions (e. g. , ask and inform) that are difficult to demonstrate. We show that the proposed framework can adapt to the user preferences (in terms of robot speed and robot verbosity), can readjust the trajectories based on the user movements, and can handle unforeseen situations. Experiments are performed in a shoe-dressing scenario. This scenario is particularly interesting because it involves a sufficient number of actions, and the human-robot interaction requires the handling of user preferences and unexpected reactions.

IROS Conference 2017 Conference Paper

Demonstration-free contextualized probabilistic movement primitives, further enhanced with obstacle avoidance

• Adrià Colomé
• Carme Torras

Movement Primitives (MPs) have been widely used over the last years for learning robot motion tasks with direct Policy Search (PS) reinforcement learning. Among them, Probabilistic Movement Primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, which benefits from the properties of probability operations. However, the generation of such ProMPs requires a set of demonstrations to capture motion variability. Additionally, using context variables to modify trajectories coded as MPs is a popular approach nowadays in order to adapt motion to environmental variables. This paper proposes a contextual representation of ProMPs that allows for an easy adaptation to changing situations through context variables, by reparametrizing motion with them. Moreover, we propose a way of initializing contextual trajectories without the need of real robot demonstrations, by setting an initial position, a final position, and a number of trajectory interest points, where the contextual variables are evaluated. The parametrizations obtained show to be accurate while relieving the user from the need of performing costly computations such as conditioning. Additionally, using this contextual representation, we propose a simple yet effective quadratic optimization-based obstacle avoidance method for ProMPs. Experiments in simulation and on a real robot show the promise of the approach.

AIJ Journal 2017 Journal Article

Efficient interactive decision-making framework for robotic applications

• Alejandro Agostini
• Carme Torras
• Florentin Wörgötter

The inclusion of robots in our society is imminent, such as service robots. Robots are now capable of reliably manipulating objects in our daily lives but only when combined with artificial intelligence (AI) techniques for planning and decision-making, which allow a machine to determine how a task can be completed successfully. To perform decision making, AI planning methods use a set of planning operators to code the state changes in the environment produced by a robotic action. Given a specific goal, the planner then searches for the best sequence of planning operators, i. e. , the best plan that leads through the state space to satisfy the goal. In principle, planning operators can be hand-coded, but this is impractical for applications that involve many possible state transitions. An alternative is to learn them automatically from experience, which is most efficient when there is a human teacher. In this study, we propose a simple and efficient decision-making framework for this purpose. The robot executes its plan in a step-wise manner and any planning impasse produced by missing operators is resolved online by asking a human teacher for the next action to execute. Based on the observed state transitions, this approach rapidly generates the missing operators by evaluating the relevance of several cause–effect alternatives in parallel using a probability estimate, which compensates for the high uncertainty that is inherent when learning from a small number of samples. We evaluated the validity of our approach in simulated and real environments, where it was benchmarked against previous methods. Humans learn in the same incremental manner, so we consider that our approach may be a better alternative to existing learning paradigms, which require offline learning, a significant amount of previous knowledge, or a large number of samples.

JMLR Journal 2017 Journal Article

Relational Reinforcement Learning for Planning with Exogenous Effects

• David Martínez
• Guillem Alenyà
• Tony Ribeiro
• Katsumi Inoue
• Carme Torras

Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive models. In contrast, learners cannot tackle yet the expressive models that planners do, which forces complex models to be mostly handcrafted. We propose a new learning approach that can learn relational probabilistic models with both action effects and exogenous effects. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. We also show how to combine this learner with reinforcement learning algorithms to solve complete problems. Finally, experimental validation is provided that shows improvements over previous work in both simulation and a robotic task. The robotic task involves a dynamic scenario with several agents where a manipulator robot has to clear the tableware on a table. We show that the exogenous effects learned by our approach allowed the robot to clear the table in a more efficient way. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

AIJ Journal 2017 Journal Article

Relational reinforcement learning with guided demonstrations

• David Martínez
• Guillem Alenyà
• Carme Torras

Model-based reinforcement learning is a powerful paradigm for learning tasks in robotics. However, in-depth exploration is usually required and the actions have to be known in advance. Thus, we propose a novel algorithm that integrates the option of requesting teacher demonstrations to learn new domains with fewer action executions and no previous knowledge. Demonstrations allow new actions to be learned and they greatly reduce the amount of exploration required, but they are only requested when they are expected to yield a significant improvement because the teacher's time is considered to be more valuable than the robot's time. Moreover, selecting the appropriate action to demonstrate is not an easy task, and thus some guidance is provided to the teacher. The rule-based model is analyzed to determine the parts of the state that may be incomplete, and to provide the teacher with a set of possible problems for which a demonstration is needed. Rule analysis is also used to find better alternative models and to complete subgoals before requesting help, thereby minimizing the number of requested demonstrations. These improvements were demonstrated in a set of experiments, which included domains from the international planning competition and a robotic task. Adding teacher demonstrations and rule analysis reduced the amount of exploration required by up to 60% in some domains, and improved the success ratio by 35% in other domains.

ICAPS Conference 2016 Conference Paper

Learning Relational Dynamics of Stochastic Domains for Planning

• David Martínez 0004
• Guillem Alenyà
• Carme Torras
• Tony Ribeiro
• Katsumi Inoue

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e. g. , moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.

IROS Conference 2015 Conference Paper

3D Sensor planning framework for leaf probing

• Sergi Foix
• Guillem Alenyà
• Carme Torras

Modern plant phenotyping requires active sensing technologies and particular exploration strategies. This article proposes a new method for actively exploring a 3D region of space with the aim of localizing special areas of interest for manipulation tasks over plants. In our method, exploration is guided by a multi-layer occupancy grid map. This map, together with a multiple-view estimator and a maximum-information-gain gathering approach, incrementally provides a better understanding of the scene until a task termination criterion is reached. This approach is designed to be applicable for any task entailing 3D object exploration where some previous knowledge of its general shape is available. Its suitability is demonstrated here for an eye-in-hand arm configuration in a leaf probing application.

ICRA Conference 2015 Conference Paper

A friction-model-based framework for Reinforcement Learning of robotic tasks in non-rigid environments

• Adrià Colomé
• Antoni Planells
• Carme Torras

Learning motion tasks in a real environment with deformable objects requires not only a Reinforcement Learning (RL) algorithm, but also a good motion characterization, a preferably compliant robot controller, and an agent giving feedback for the rewards/costs in the RL algorithm. In this paper, we unify all these parts in a simple but effective way to properly learn safety-critical robotic tasks such as wrapping a scarf around the neck (so far, of a mannequin).

EAAI Journal 2015 Journal Article

Planning robot manipulation to clean planar surfaces

• David Martínez
• Guillem Alenyà
• Carme Torras

This paper presents a new approach to plan high-level manipulation actions for cleaning surfaces in household environments, like removing dirt from a table using a rag. Dragging actions can change the distribution of dirt in an unpredictable manner, and thus the planning becomes challenging. We propose to define the problem using explicitly uncertain actions, and then plan the most effective sequence of actions in terms of time. However, some issues have to be tackled to plan efficiently with stochastic actions. States become hard to predict after executing a few actions, so replanning every few actions with newer perceptions gives the best results, and the trade-off between planning time and plan quality is also important. Finally a learner is integrated to provide adaptation to changes, such as different rag grasps, robots, or cleaning surfaces. We demonstrate experimentally, using two different robot platforms, that planning is more advantageous than simple reactive strategies for accomplishing complex tasks, while still providing a similar performance for easy tasks. We also performed experiments where the rag grasp was changed, and thus the behaviour of the dragging actions, showing that the learning capabilities allow the robot to double its performance with a new rag grasp after a few cleaning iterations.

IROS Conference 2015 Conference Paper

Safe robot execution in model-based reinforcement learning

• David Martínez 0004
• Guillem Alenyà
• Carme Torras

Task learning in robotics requires repeatedly executing the same actions in different states to learn the model of the task. However, in real-world domains, there are usually sequences of actions that, if executed, may produce unrecoverable errors (e. g. breaking an object). Robots should avoid repeating such errors when learning, and thus explore the state space in a more intelligent way. This requires identifying dangerous action effects to avoid including such actions in the generated plans, while at the same time enforcing that the learned models are complete enough for the planner not to fall into dead-ends.

AAAI Conference 2015 Conference Paper

V-MIN: Efficient Reinforcement Learning through Demonstrations and Relaxed Reward Demands

• David Martínez
• Guillem Alenyà
• Carme Torras

Reinforcement learning (RL) is a common paradigm for learning tasks in robotics. However, a lot of exploration is usually required, making RL too slow for high-level tasks. We present V-MIN, an algorithm that integrates teacher demonstrations with RL to learn complex tasks faster. The algorithm combines active demonstration requests and autonomous exploration to find policies yielding rewards higher than a given threshold Vmin. This threshold sets the degree of quality with which the robot is expected to complete the task, thus allowing the user to either opt for very good policies that require many learning experiences, or to be more permissive with sub-optimal policies that are easier to learn. The threshold can also be increased online to force the system to improve its policies until the desired behavior is obtained. Furthermore, the algorithm generalizes previously learned knowledge, adapting well to changes. The performance of V-MIN has been validated through experimentation, including domains from the international planning competition. Our approach achieves the desired behavior where previous algorithms failed.

AIJ Journal 2015 Journal Article

Variable symmetry breaking in numerical constraint problems

• Alexandre Goldsztejn
• Christophe Jermann
• Vicente Ruiz de Angulo
• Carme Torras

Symmetry breaking has been a hot topic of research in the past years, leading to many theoretical developments as well as strong scaling strategies for dealing with hard applications. Most of the research has however focused on discrete, combinatorial, problems, and only few considered also continuous, numerical, problems. While part of the theory applies in both contexts, numerical problems have specificities that make most of the technical developments inadequate. In this paper, we present the rlex constraints, partial symmetry-breaking inequalities corresponding to a relaxation of the famous lex constraints extensively studied in the discrete case. They allow (partially) breaking any variable symmetry and can be generated in polynomial time. Contrarily to lex constraints that are impractical in general (due to their overwhelming number) and inappropriate in the continuous context (due to their form), rlex constraints can be efficiently handled natively by numerical constraint solvers. Moreover, we demonstrate their pruning power on continuous domains is almost as strong as that of lex constraints, and they subsume several previous work on breaking specific symmetry classes for continuous problems. Their experimental behavior is assessed on a collection of standard numerical problems and the factors influencing their impact are studied. The results confirm rlex constraints are a dependable counterpart to lex constraints for numerical problems.

ICRA Conference 2014 Conference Paper

Active learning of manipulation sequences

• David Martínez 0004
• Guillem Alenyà
• Pablo Jiménez
• Carme Torras
• Jürgen Roßmann
• Nils Wantia
• Eren Erdal Aksoy
• Simon Haller

We describe a system allowing a robot to learn goal-directed manipulation sequences such as steps of an assembly task. Learning is based on a free mix of exploration and instruction by an external teacher, and may be active in the sense that the system tests actions to maximize learning progress and asks the teacher if needed. The main component is a symbolic planning engine that operates on learned rules, defined by actions and their pre- and postconditions. Learned by model-based reinforcement learning, rules are immediately available for planning. Thus, there are no distinct learning and application phases. We show how dynamic plans, replanned after every action if necessary, can be used for automatic execution of manipulation sequences, for monitoring of observed manipulation sequences, or a mix of the two, all while extending and refining the rule base on the fly. Quantitative results indicate fast convergence using few training examples, and highly effective teacher intervention at early stages of learning.

IROS Conference 2014 Conference Paper

Dimensionality reduction and motion coordination in learning trajectories with Dynamic Movement Primitives

• Adrià Colomé
• Carme Torras

Dynamic Movement Primitives (DMP) are nowadays widely used as movement parametrization for learning trajectories, because of their linearity in the parameters, rescaling robustness and continuity. However, when learning a movement with a robot using DMP, many parameters may need to be tuned, requiring a prohibitive number of experiments/simulations to converge to a solution with a locally or globally optimal reward.

EAAI Journal 2014 Journal Article

Learning RGB-D descriptors of garment parts for informed robot grasping

• Arnau Ramisa
• Guillem Alenyà
• Francesc Moreno-Noguer
• Carme Torras

Robotic handling of textile objects in household environments is an emerging application that has recently received considerable attention thanks to the development of domestic robots. Most current approaches follow a multiple re-grasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields a desired configuration. In this work we propose a vision-based method, built on the Bag of Visual Words approach, that combines appearance and 3D information to detect parts suitable for grasping in clothes, even when they are highly wrinkled. We also contribute a new, annotated, garment part dataset that can be used for benchmarking classification, part detection, and segmentation algorithms. The dataset is used to evaluate our approach and several state-of-the-art 3D descriptors for the task of garment part detection. Results indicate that appearance is a reliable source of information, but that augmenting it with 3D information can help the method perform better with new clothing items.

EAAI Journal 2014 Journal Article

Learning weakly correlated cause–effects for gardening with a cognitive system

• Alejandro Agostini
• Carme Torras
• Florentin Wörgötter

We propose a cognitive system that combines artificial intelligence techniques for planning and learning to execute tasks involving delayed and variable correlations between the actions executed and their expected effects. The system is applied to the task of controlling the growth of plants, where the evolution of the plant attributes strongly depends on different events taking place in the temporally distant past history of the plant. The main problem to tackle is how to efficiently detect these past events. This is very challenging since the inclusion of time could make the dimensionality of the search space extremely large and the collected training instances may only provide very limited information about the relevant combinations of events. To address this problem we propose a learning method that progressively identifies those events that are more likely to produce a sequence of changes under a plant treatment. Since the number of experiences is very limited compared to the size of the event space, we use a probabilistic estimate that takes into account the lack of experience to prevent biased estimations. Planning operators are generated from most accurately predicted sequences of changes. Planning and learning are integrated in a decision-making framework that operates without task interruptions by allowing a human gardener to instruct the treatments when the knowledge acquired so far is not enough to make a decision.

ICRA Conference 2014 Conference Paper

Realtime tracking and grasping of a moving object from range video

• Farzad Husain
• Adrià Colomé
• Babette Dellen
• Guillem Alenyà
• Carme Torras

In this paper we present an automated system that is able to track and grasp a moving object within the workspace of a manipulator using range images acquired with a Microsoft Kinect sensor. Realtime tracking is achieved by a geometric particle filter on the affine group. Based on the tracked output, the pose of a 7-DoF WAM robotic arm is continuously updated using dynamic motor primitives until a distance measure between the tracked object and the gripper mounted on the arm is below a threshold. Then, it closes its three fingers and grasps the object. The tracker works in realtime and is robust to noise and partial occlusions. Using only the depth data makes our tracker independent of texture which is one of the key design goals in our approach. An experimental evaluation is provided along with a comparison of the proposed tracker with state-of-the-art approaches, including the OpenNI-tracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.

ICRA Conference 2013 Conference Paper

External force estimation during compliant robot manipulation

• Adrià Colomé
• Diego Pardo
• Guillem Alenyà
• Carme Torras

This paper presents a method to estimate external forces exerted on a manipulator during motion, avoiding the use of a sensor. The method is based on task-oriented dynamics model learning and a robust disturbance state observer. The combination of both leads to an efficient torque observer that can be incorporated to any control scheme. The use of a learning-based approach avoids the need of analytical models of joints' friction or Coriolis dynamics effects.

IROS Conference 2013 Conference Paper

FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation

• Arnau Ramisa
• Guillem Alenyà
• Francesc Moreno-Noguer
• Carme Torras

Most current depth sensors provide 2. 5D range images in which depth values are assigned to a rectangular 2D array. In this paper we take advantage of this structured information to build an efficient shape descriptor which is about two orders of magnitude faster than competing approaches, while showing similar performance in several tasks involving deformable object recognition. Given a 2D patch surrounding a point and its associated depth values, we build the descriptor for that point, based on the cumulative distances between their normals and a discrete set of normal directions. This processing is made very efficient using integral images, even allowing to compute descriptors for every range image pixel in a few seconds. The discriminative power of our descriptor, dubbed FINDDD, is evaluated in three different scenarios: recognition of specific cloth wrinkles, instance recognition from geometry alone, and detection of reliable and informed grasping points.

AAAI Conference 2013 Conference Paper

Learning Collaborative Impedance-Based Robot Behaviors

• Leonel Rozo
• Sylvain Calinon
• Darwin Caldwell
• Pablo Jimenez
• Carme Torras

Research in learning from demonstration has focused on transferring movements from humans to robots. However, a need is arising for robots that do not just replicate the task on their own, but that also interact with humans in a safe and natural way to accomplish tasks cooperatively. Robots with variable impedance capabilities opens the door to new challenging applications, where the learning algorithms must be extended by encapsulating force and vision information. In this paper we propose a framework to transfer impedancebased behaviors to a torque-controlled robot by kinesthetic teaching. The proposed model encodes the examples as a task-parameterized statistical dynamical system, where the robot impedance is shaped by estimating virtual stiffness matrices from the set of demonstrations. A collaborative assembly task is used as testbed. The results show that the model can be used to modify the robot impedance along task execution to facilitate the collaboration, by triggering stiff and compliant behaviors in an on-line manner to adapt to the user’s actions.

IROS Conference 2012 Conference Paper

POMDP approach to robotized clothes separation

• Pol Monso
• Guillem Alenyà
• Carme Torras

Rigid object manipulation with robots has mainly relied on precise, expensive models and deterministic sequences. Given the great complexity of accurately modeling deformable objects, their manipulation seems to call for a rather different approach. This paper proposes a probabilistic planner, based on a Partially Observable Markov Decision Process (POMDP), targeted at reducing the inherent uncertainty of deformable object sorting. It is shown that a small set of unreliable actions and inaccurate perceptions suffices to accomplish the task, provided faithful statistics on both of them are collected beforehand. The planner has been applied to a clothes sorting task in a real case context with a depth and color sensor and a robotic arm. Experimental results show the promise of the approach since more than 95% certainty of having isolated a piece of clothing is reached in an average of four steps for quite entangled initial clothing configurations.

IROS Conference 2012 Conference Paper

Redundant inverse kinematics: Experimental comparative review and two enhancements

• Adrià Colomé
• Carme Torras

Motivated by the need of a robust and practical Inverse Kinematics (IK) algorithm for the WAM robot arm, we reviewed the most used closed-loop methods for redundant robots, analysing their main points of concern: convergence, numerical error, singularity handling, joint limit avoidance, and the capability of reaching secondary goals. As a result of the experimental comparison, we propose two enhancements. The first is to filter the singular values of the Jacobian matrix before calculating its pseudoinverse in order to obtain a more numerically robust result. The second is to combine a continuous task priority strategy with selective damping to generate smoother trajectories. Experimentation on the WAM robot arm shows that these two enhancements yield an IK algorithm that improves on the reviewed state-of-the-art ones, in terms of the good compromise it achieves between time step length, Jacobian conditioning, multiple task performance, and computational time, thus constituting a very solid option in practice. This proposal is general and applicable to other redundant robots.

ICRA Conference 2012 Conference Paper

Using depth and appearance features for informed robot grasping of highly wrinkled clothes

• Arnau Ramisa
• Guillem Alenyà
• Francesc Moreno-Noguer
• Carme Torras

Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple re-grasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points, generally in one single step, even when clothes are highly wrinkled. In order to handle the large variability a deformed cloth may have, we build a Bag of Features based detector that combines appearance and 3D geometry features. An image is scanned using a sliding window with a linear classifier, and the candidate windows are refined using a non-linear SVM and a “grasp goodness” criterion to select the best grasping point. We demonstrate our approach detecting collars in deformed polo shirts, using a Kinect camera. Experimental results show a good performance of the proposed method not only in identifying the same trained textile object part under severe deformations and occlusions, but also the corresponding part in other clothes, exhibiting a degree of generalization.

ICRA Conference 2011 Conference Paper

3D modelling of leaves from color and ToF data for robotized plant measuring

• Guillem Alenyà
• Babette Dellen
• Carme Torras

Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modelling for this application has strong demands, particularly in what concerns 3D information gathering and speed. This paper shows that Time-of-Flight (ToF) cameras achieve a good compromise between both demands, providing a suitable complement to color vision. A new method is proposed to segment plant images into their composite surface patches by combining hierarchical color segmentation with quadratic surface fitting using ToF depth data. Experimentation shows that the interpolated depth maps derived from the obtained surfaces fit well the original scenes. Moreover, candidate leaves to be approached by a measuring instrument are ranked, and then robot-mounted cameras move closer to them to validate their suitability to being sampled. Some ambiguities arising from leaves overlap or occlusions are cleared up in this way. The work is a proof-of-concept that dense color data combined with sparse depth as provided by a ToF camera yields a good enough 3D approximation for automated plant measuring at the high throughput imposed by the application.

IJCAI Conference 2011 Conference Paper

Integrating Task Planning and Interactive Learning for Robots to Work in Human Environments

• Alejandro Agostini
• Carme Torras
• Florentin W
• ouml; rg
• ouml; tter

Human environments are challenging for robots, which need to be trainable by lay people and learn new behaviours rapidly without disrupting much the ongoing activity. A system that integrates AI techniques for planning and learning is here proposed to satisfy these strong demands. The approach rapidly learns planning operators from few action experiences using a competitive strategy where many alternatives of cause-effect explanations are evaluated in parallel, and the most successful ones are used to generate the operators. The success of a cause-effect explanation is evaluated by a probabilistic estimate that compensates the lack of experience, producing more confident estimations and speeding up the learning in relation to other known estimates. The system operates without task interruption by integrating in the planning-learning loop a human teacher that supports the planner in making decisions. All the mechanisms are integrated and synchronized in the robot using a general decision-making framework. The feasibility and scalability of the architecture are evaluated in two different robot platforms: a Stä ubli arm, and the humanoid ARMAR III.

ICRA Conference 2010 Conference Paper

A family of quadratically-solvable 5-SPU parallel robots

• Júlia Borràs 0001
• Federico Thomas
• Carme Torras

A 5-SPU robot with collinear universal joints is well suited to handling an axisymmetric tool, since it has 5 controllable DoFs and the remaining one is a free rotation around the tool. The kinematics of such a robot having also coplanar spherical joints has previously been studied as a rigid subassembly of a Stewart-Gough platform, it being denoted a line-plane component. It was shown that this component has 8 assembly modes corresponding to the roots of a bi-quartic polynomial. Here we identify a whole family of these 5-SPU robots having only 4 assembly modes, which are obtained by solving two quadratic equations. This family is defined by a simple proportionality constraint relating the coordinates of the base and platform attachments. A geometric interpretation of the architectural singularities of this type of robots in terms of conics is provided, which facilitates their avoidance at the design stage. Parallel singularities obey also a neat geometric structure, which permits deriving a cell decomposition of configuration space. Two practical features of these quadratically-solvable robots are the large maneuverability within each connected component and the fact that, for a fixed orientation of the tool, the singularity locus reduces to a plane.

ICRA Conference 2010 Conference Paper

Object modeling using a ToF camera under an uncertainty reduction approach

• Sergi Foix
• Guillem Alenyà
• Juan Andrade-Cetto
• Carme Torras

Time-of-Flight (ToF) cameras deliver 3D images at 25 fps, offering great potential for developing fast object modeling algorithms. Surprisingly, this potential has not been extensively exploited up to now. A reason for this is that, since the acquired depth images are noisy, most of the available registration algorithms are hardly applicable. A further difficulty is that the transformations between views are in general not accurately known, a circumstance that multi-view object modeling algorithms do not handle properly under noisy conditions. In this work, we take into account both uncertainty sources (in images and camera poses) to generate spatially consistent 3D object models fusing multiple views with a probabilistic approach. We propose a method to compute the covariance of the registration process, and apply an iterative state estimation method to build object models under noisy conditions.

ICRA Conference 2008 Conference Paper

Architecture singularities in flagged parallel manipulators

• Júlia Borràs 0001
• Federico Thomas
• Carme Torras

Flagged manipulators are of interest because they are the only Stewart-Gough platforms for which a cell decomposition of their singularity loci is available. Here we show that the known family of such manipulators can be enlarged if one allows robot designs that, for some particular parameter values, become architecturally singular. Along this line, the most general 6-6 flagged manipulator is derived by applying a singularity-preserving transformation that leaves the relative position between two lines invariant. This transformation opens up the possibility of an "equal cross ratios" architectural singularity, which is shown to appear clearly in the factorization of the Jacobian determinant. From the 6-6 flagged manipulator, all the extended family of (possibly architecturally-singular) flagged manipulators is derived.

ICRA Conference 2007 Conference Paper

Depth from the visual motion of a planar target induced by zooming

• Guillem Alenyà
• Maria Alberich-Carramiñana
• Carme Torras

Robot egomotion can be estimated from an acquired video stream up to the scale of the scene. To remove this uncertainty (and obtain true egomotion), a distance within the scene needs to be known. If no a priori knowledge on the scene is assumed, the usual solution is to derive "in some way" the initial distance from the camera to a target object. This paper proposes a new, very simple way to obtain such a distance, when a zooming camera is available and there is a planar target in the scene. Similarly to "two-grid calibration" algorithms, no estimation of the camera parameters is required, and no assumption on the optical axis stability between the different focal lengths is needed. Quite the reverse, the non stability of the optical axis between the different focal lengths is the key ingredient that enables to derive our depth estimate, by applying a result in projective geometry. Experiments carried out on a mobile robot platform show the promise of the approach.

ICRA Conference 2007 Conference Paper

Outdoor Landmark-view Recognition Based on Bipartite-graph Matching and Logistic Regression

• Eduardo Todt
• Carme Torras

This paper describes the extraction of visual landmarks from outdoor images for mobile robot applications. The concept of group of landmarks, called landmark-view, is introduced, aggregating the most relevant landmarks present in each scene. The relevance of the landmarks is determined by their relative visual saliency. Thus, landmark co-occurrence and spatial and saliency relationships between them are added to the single landmark descriptors, which are based on saliency and color distribution in chromaticity space. A suitable framework to compare landmark-views is developed, and it is shown how this remarkably enhances the recognition performance, compared against the single landmark recognition. A view-matching model is constructed using logistic regression. Experimentation using 45 views, acquired outdoors, containing 273 landmarks, yielded good recognition results. Of the 42 corresponding view pairs, 30 were recognized correctly, resulting in 71. 4% of correct classification of similar views. Of the 948 non-corresponding view pairs, 768 were recognized correctly, resulting in 81. 0% of correct classification in non-similar views. The overall percentage of correct view classification obtained was 80. 6%, indicating the convenience of the approach.

ICRA Conference 2006 Conference Paper

On Redundant Flagged Manipulators

• Maria Alberich-Carramiñana
• Federico Thomas
• Carme Torras

Flagged in-parallel manipulators are attractive because their singularity loci admit a well-behaved decomposition, with a unique topology irrespective of the metrics of each particular design. In this paper, this topology is formally derived and all the cells, in the configuration space of the platform, of dimension 6 (non-singular) and dimension 5 (singular), together with their adjacencies, are worked out in detail. This characterization of the singularity loci is useful to come up with designs which admit control strategies free of singularities. In particular, it is shown that by adding an extra leg to any flagged manipulator, the resulting 7-leg structure admits a control strategy (by appropriately choosing which leg remains passive) that completely avoids singularities

ICRA Conference 2005 Conference Paper

Using Laser and Vision to Locate a Robot in an Industrial Environment: A Practical Experience

• Guillem Alenyà
• Josep Escoda
• Antonio B. Martínez
• Carme Torras

The fully flexible navigation of autonomous vehicles in industrial environments is still unsolved. It is hard to conciliate strict precision requirements with quick adaptivity to new settings without undergoing costly rearrangements. We are pursuing a research project trying to combine the precision of laser-based local positioning with the flexibility of vision-based robot motion estimation. An enhanced circle approach to dynamic triangulation combining laser and odometric signals has been used to improve positioning accuracy. As regards to vision, a novel technique relating the deformation of contours in an image sequence to the 3D motion underwent by the camera has been developed. Interestingly, contours are fitted to objects already present in the environment, without requiring any presetting. In this paper, we describe a practical experience conducted in the warehouse of a beer production factory in Barcelona. A database containing the laser readings, image sequences and robot odometry along several trajectories was compiled, and subsequently processed off-line in order to assess the accuracies of both techniques under a variety of circumstances. In all, vision-based estimation turned out to be about one order of magnitude less precise than laser-based positioning, which qualifies the vision-based technique as a promising alternative to accomplish robot transfers across long distances, such as those needed in a warehouse, while backing up on laser-based positioning when accurate docking for loading and unloading operations is needed.

TCS Journal 2004 Journal Article

Neural learning methods yielding functional invariance

• Vicente Ruiz de Angulo
• Carme Torras

This paper investigates the functional invariance of neural network learning methods incorporating a complexity reduction mechanism, such as a regularizer. By functional invariance we mean the property of producing functionally equivalent minima as the size of the network grows, when the smoothing parameters are fixed. We study three different principles on which functional invariance can be based, and try to delimit the conditions under which each of them acts. We find out that, surprisingly, some of the most popular neural learning methods, such as weight-decay and input noise addition, exhibit this interesting property.

ICRA Conference 2003 Conference Paper

A Branch-and-Prune Algorithm for Solving Systems of Distance Constraints

• Josep M. Porta
• Federico Thomas
• Lluís Ros
• Carme Torras

Given a set of affine varieties in R/sup 3/, i. e. planes, lines, and points, the problem tackled in this paper is that of finding all possible configurations for these varieties that satisfy a set of pairwise euclidean distances between them. Many problems in robotics - such as the forward kinematics of patroller manipulators or the contact formation problem between polyhedral models - can be formulated in this way. We propose herein a strategy that consists in finding some distances, that are unknown a priori, and whose derivation permits solving the problem rather trivially. Finding these distances relies on a branch-and-prune technique that iteratively eliminates from the space of distances entire regions which cannot contain any solution. The elimination is accomplished by applying redundant necessary conditions derived from the theory of Cayley-Menger determinants. The experimental results obtained qualify this approach as a promising one.

ICRA Conference 1999 Conference Paper

Detection Between Nonconvex Polyhedral Models

• Pablo Jiménez
• Carme Torras

Nonconvex polyhedral models of workpieces or robot parts can be directly tested for interference, without resorting to a previous decomposition into convex entities. We show that this interference detection, based on the elemental edge face intersection test, can be performed efficiently: a strategy based on applicability constraints reduces drastically the set of edge - face pairings that have to be considered for intersection. This is accomplished by using an appropriate representation, the spherical face orientation graph, developed by the authors, as well as feature pairing algorithms based on the plane sweep paradigm that have been adapted to work on that representation. Furthermore, the benefits of such a strategy extend to the computation of a lower distance bound between the polyhedra, both lowering the computational effort and improving the quality of the bound. Experimental results confirm the expected advantages of this strategy.

ICRA Conference 1998 Conference Paper

Selection of Image Features for Robot Positioning using Mutual Information

• Gordon Wells
• Carme Torras

The authors and Venaille (1996) developed a prototype for visual robot positioning, based on global image descriptors and neural networks. Now, a procedure to automatically select subsets of image features most relevant to determine pose variations along each of the six degrees of freedom (DOFs) has been incorporated into the prototype. This procedure is based on a statistical measure of variable interdependence, called mutual information. Three families of features are considered in this paper: geometric moments, eigenfeatures and pose-image covariance vectors. The experimental results described show the quantitative and qualitative benefits of carrying out this feature selection prior to training the neural network: fewer network inputs need to be considered, thus considerably shortening training times; the DOFs that would yield larger errors can be determined beforehand, so that more informative features can be looked for; the ordering of the features selected for each DOF often admits a very natural interpretation, which in turn helps to provide insights for devising features tailored to each DOF.

ICRA Conference 1996 Conference Paper

Speeding up interference detection between polyhedra

• Pablo Jiménez
• Carme Torras

A classical paradigm for interference detection between polyhedra consists in testing all edges of one polyhedron against all faces of the other one for intersection. If the relative orientation of the polyhedra is fixed, only certain edge-face pairs can intersect first, when the polyhedra come into contact. These candidate pairs are efficiently determined using a representation which the authors call spherical face orientation graph. By applying the interference test to candidates only, the computational effort is significantly reduced, as shown by experimental results with convex polyhedra. In the non-convex case, the strategy is conservative, but it still leads to savings.

IROS Conference 1994 Conference Paper

Efficient reinforcement learning of navigation strategies in an autonomous robot

• José del R. Millán
• Carme Torras

Proposes a reinforcement learning architecture that allows an autonomous robot to acquire efficient navigation strategies in a few trials. Besides fast learning, the architecture has 3 further appealing features. (1) Since it learns from built-in reflexes, the robot is operational from the very beginning. (2) The robot improves its performance incrementally as it interacts with an initially unknown environment, and it ends up learning to avoid collisions even if its sensors cannot detect the obstacles. This is a definite advantage over non-learning reactive robots. (3) The robot exhibits high tolerance to noisy sensory data and good generalization abilities. All these features make this learning robot's architecture very well suited to real-world applications. The authors report experimental results obtained with a real mobile robot in an indoor environment that demonstrate the feasibility of this approach. >

ICRA Conference 1994 Conference Paper

Interference Detection Between Non-Convex Polyhedra Revisited with a Practical Aim

• Federico Thomas
• Carme Torras

Exact interference checking between two arbitrary polyhedra is known to have O(mn) complexity, where m and n are the number of edges in the two polyhedra. This is just a worst-case bound that still leaves plenty of room for algorithm improvement in practice. The algorithm presented herein has been developed so as to: 1. Minimize the number of operations that each pairing of edges entails. We prove that this factor is 4. 5 for multiplications and 8. 5 for additions. 2. Avoid the construction of auxiliary geometric entities. The standard approach is to decompose the nonconvex polyhedra (or their faces) into convex entities and then check for interference in this convex setting. This entails the construction of many fictitious edges and faces, which indirectly contribute to the growth of the complexity. 3. Permit the straightforward application of prunning strategies to most practical situations, so that the worst-case bound above is reached only when truly needed. 4 Allow the derivation of both directional and undirectional distance bounds between the polyhedra, which prove extremely useful for collision avoidance and local path planning. The simplicity and homogeneity of the algorithm has led to a quick implementation, which has been proven to be robust and fast. Some performance measurements are reported. >

ECAI Conference 1994 Conference Paper

Neural Learning for Robot Control

• Carme Torras

IROS Conference 1992 Conference Paper

Learning To Avoid Obstacles Through Reinforcement: Noise-tolerance, Generalization And Dynamic Capabilities

• José del R. Millán
• Carme Torras

ECAI Conference 1990 Conference Paper

Finding Object Configurations that Satisfy Spatial Relationships

• Enric Celaya
• Carme Torras

ECAI Conference 1990 Conference Paper

Reinforcement Learning: Discovering Stable Solutions in the Robot Path Finding Domain

• José del R. Millán
• Carme Torras

ICRA Conference 1988 Conference Paper

A least-commitment approach to intelligent robotic assembly

• Luis Basañez
• Robert B. Kelley
• Michael C. Moed
• Carme Torras

The authors propose a robotic assembly system that is based on the least-commitment principle, in which the successive stages proceeding from planning to execution become progressively more specific. The system is partitioned into an offline automatic programming system and an online execution manager. The framework and objectives are presented, followed by an overview of the global system. The submodules of a system that has actually executed a simple pick/place task are described. Some conclusions and perspectives are outlined. >

ICRA Conference 1988 Conference Paper

Constraint-based interference of assembly configurations

• Federico Thomas
• Carme Torras

A system for the automatic synthesis of assembly configuration is presented. It consists of the propagation, combination, and satisfaction of three types of constraints: shape-matching constraints, constraints on the degrees of freedom, and nonintersection constraints. Given a high-level description of an assembly and the models of the workpieces, the system determines which parts of the workpieces should be mated and produces a set of homogeneous-coordinate transformations defining the relative position and orientation of each workpiece in the final assembly. This system can be seen as a previous step towards a practical and efficient assembly planner. >