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79 results (View BibTeX file of all listed publications)

2012

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The Balancing Cube: A Dynamic Sculpture as Test Bed for Distributed Estimation and Control

Trimpe, S., D’Andrea, R.

IEEE Control Systems Magazine, 32(6):48-75, December 2012 (article)

DOI [BibTex]

2012

Trimpe, S., D’Andrea, R. The Balancing Cube: A Dynamic Sculpture as Test Bed for Distributed Estimation and Control IEEE Control Systems Magazine, 32(6):48-75, December 2012 (article)

DOI [BibTex]

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Towards Multi-DOF model mediated teleoperation: Using vision to augment feedback

Willaert, B., Bohg, J., Van Brussel, H., Niemeyer, G.

In IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE), pages: 25-31, October 2012 (inproceedings)

Abstract
In this paper, we address some of the challenges that arise as model-mediated teleoperation is applied to systems with multiple degrees of freedom and multiple sensors. Specifically we use a system with position, force, and vision sensors to explore an environment geometry in two degrees of freedom. The inclusion of vision is proposed to alleviate the difficulties of estimating an increasing number of environment properties. Vision can furthermore increase the predictive nature of model-mediated teleoperation, by effectively predicting touch feedback before the slave is even in contact with the environment. We focus on the case of estimating the location and orientation of a local surface patch at the contact point between the slave and the environment. We describe the various information sources with their respective limitations and create a combined model estimator as part of a multi-d.o.f. model-mediated controller. An experiment demonstrates the feasibility and benefits of utilizing vision sensors in teleoperation.

DOI [BibTex]

Willaert, B., Bohg, J., Van Brussel, H., Niemeyer, G. Towards Multi-DOF model mediated teleoperation: Using vision to augment feedback In IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE), pages: 25-31, October 2012 (inproceedings)

DOI [BibTex]

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Failure Recovery with Shared Autonomy

Sankaran, B., Pitzer, B., Osentoski, S.

In International Conference on Intelligent Robots and Systems, October 2012 (inproceedings)

Abstract
Building robots capable of long term autonomy has been a long standing goal of robotics research. Such systems must be capable of performing certain tasks with a high degree of robustness and repeatability. In the context of personal robotics, these tasks could range anywhere from retrieving items from a refrigerator, loading a dishwasher, to setting up a dinner table. Given the complexity of tasks there are a multitude of failure scenarios that the robot can encounter, irrespective of whether the environment is static or dynamic. For a robot to be successful in such situations, it would need to know how to recover from failures or when to ask a human for help. This paper, presents a novel shared autonomy behavioral executive to addresses these issues. We demonstrate how this executive combines generalized logic based recovery and human intervention to achieve continuous failure free operation. We tested the systems over 250 trials of two different use case experiments. Our current algorithm drastically reduced human intervention from 26% to 4% on the first experiment and 46% to 9% on the second experiment. This system provides a new dimension to robot autonomy, where robots can exhibit long term failure free operation with minimal human supervision. We also discuss how the system can be generalized.

link (url) [BibTex]

Sankaran, B., Pitzer, B., Osentoski, S. Failure Recovery with Shared Autonomy In International Conference on Intelligent Robots and Systems, October 2012 (inproceedings)

link (url) [BibTex]

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Task-Based Grasp Adaptation on a Humanoid Robot

Bohg, J., Welke, K., León, B., Do, M., Song, D., Wohlkinger, W., Aldoma, A., Madry, M., Przybylski, M., Asfour, T., Marti, H., Kragic, D., Morales, A., Vincze, M.

In 10th IFAC Symposium on Robot Control, SyRoCo 2012, Dubrovnik, Croatia, September 5-7, 2012., pages: 779-786, September 2012 (inproceedings)

Abstract
In this paper, we present an approach towards autonomous grasping of objects according to their category and a given task. Recent advances in the field of object segmentation and categorization as well as task-based grasp inference have been leveraged by integrating them into one pipeline. This allows us to transfer task-specific grasp experience between objects of the same category. The effectiveness of the approach is demonstrated on the humanoid robot ARMAR-IIIa.

Video pdf DOI [BibTex]

Bohg, J., Welke, K., León, B., Do, M., Song, D., Wohlkinger, W., Aldoma, A., Madry, M., Przybylski, M., Asfour, T., Marti, H., Kragic, D., Morales, A., Vincze, M. Task-Based Grasp Adaptation on a Humanoid Robot In 10th IFAC Symposium on Robot Control, SyRoCo 2012, Dubrovnik, Croatia, September 5-7, 2012., pages: 779-786, September 2012 (inproceedings)

Video pdf DOI [BibTex]

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Visual Servoing on Unknown Objects

Gratal, X., Romero, J., Bohg, J., Kragic, D.

Mechatronics, 22(4):423-435, Elsevier, June 2012, Visual Servoing \{SI\} (article)

Abstract
We study visual servoing in a framework of detection and grasping of unknown objects. Classically, visual servoing has been used for applications where the object to be servoed on is known to the robot prior to the task execution. In addition, most of the methods concentrate on aligning the robot hand with the object without grasping it. In our work, visual servoing techniques are used as building blocks in a system capable of detecting and grasping unknown objects in natural scenes. We show how different visual servoing techniques facilitate a complete grasping cycle.

Grasping sequence video Offline calibration video Pdf DOI [BibTex]

Gratal, X., Romero, J., Bohg, J., Kragic, D. Visual Servoing on Unknown Objects Mechatronics, 22(4):423-435, Elsevier, June 2012, Visual Servoing \{SI\} (article)

Grasping sequence video Offline calibration video Pdf DOI [BibTex]

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Movement Segmentation and Recognition for Imitation Learning

Meier, F., Theodorou, E., Schaal, S.

In Seventeenth International Conference on Artificial Intelligence and Statistics, La Palma, Canary Islands, Fifteenth International Conference on Artificial Intelligence and Statistics , April 2012 (inproceedings)

link (url) [BibTex]

Meier, F., Theodorou, E., Schaal, S. Movement Segmentation and Recognition for Imitation Learning In Seventeenth International Conference on Artificial Intelligence and Statistics, La Palma, Canary Islands, Fifteenth International Conference on Artificial Intelligence and Statistics , April 2012 (inproceedings)

link (url) [BibTex]

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Emotionally Assisted Human-Robot Interaction Using a Wearable Device for Reading Facial Expressions

Gruebler, A., Berenz, V., Suzuki, K.

Advanced Robotics, 26(10):1143-1159, 2012 (article)

link (url) DOI [BibTex]

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From Dynamic Movement Primitives to Associative Skill Memories

Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J., Theodorou, E., Schaal, S.

Robotics and Autonomous Systems, 2012 (article)

Project Page [BibTex]

Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J., Theodorou, E., Schaal, S. From Dynamic Movement Primitives to Associative Skill Memories Robotics and Autonomous Systems, 2012 (article)

Project Page [BibTex]

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Event-based State Estimation with Switching Static-gain Observers

Trimpe, S.

In Proceedings of the 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2012 (inproceedings)

PDF DOI [BibTex]

Trimpe, S. Event-based State Estimation with Switching Static-gain Observers In Proceedings of the 3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2012 (inproceedings)

PDF DOI [BibTex]

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Usability benchmarks of the Targets-Drives-Means robotic architecture

Berenz, V., Suzuki, K.

In 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), Osaka, Japan, November 29 - Dec. 1, 2012, pages: 514-519, 2012 (inproceedings)

link (url) DOI [BibTex]

Berenz, V., Suzuki, K. Usability benchmarks of the Targets-Drives-Means robotic architecture In 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), Osaka, Japan, November 29 - Dec. 1, 2012, pages: 514-519, 2012 (inproceedings)

link (url) DOI [BibTex]

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Event-based State Estimation with Variance-Based Triggering

Trimpe, S., D’Andrea, R.

In Proceedings of the 51st IEEE Conference on Decision and Control, 2012 (inproceedings)

PDF Supplementary material DOI [BibTex]

Trimpe, S., D’Andrea, R. Event-based State Estimation with Variance-Based Triggering In Proceedings of the 51st IEEE Conference on Decision and Control, 2012 (inproceedings)

PDF Supplementary material DOI [BibTex]

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Inverse dynamics with optimal distribution of contact forces for the control of legged robots

Righetti, L., Schaal, S.

In Dynamic Walking 2012, Pensacola, 2012 (inproceedings)

[BibTex]

Righetti, L., Schaal, S. Inverse dynamics with optimal distribution of contact forces for the control of legged robots In Dynamic Walking 2012, Pensacola, 2012 (inproceedings)

[BibTex]

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Autonomous battery management for mobile robots based on risk and gain assessment

Berenz, V., Tanaka, F., Suzuki, K.

Artif. Intell. Rev., 37(3):217-237, 2012 (article)

link (url) DOI [BibTex]

Berenz, V., Tanaka, F., Suzuki, K. Autonomous battery management for mobile robots based on risk and gain assessment Artif. Intell. Rev., 37(3):217-237, 2012 (article)

link (url) DOI [BibTex]

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Encoding of Periodic and their Transient Motions by a Single Dynamic Movement Primitive

Ernesti, J., Righetti, L., Do, M., Asfour, T., Schaal, S.

In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pages: 57-64, IEEE, Osaka, Japan, November 2012 (inproceedings)

link (url) DOI [BibTex]

Ernesti, J., Righetti, L., Do, M., Asfour, T., Schaal, S. Encoding of Periodic and their Transient Motions by a Single Dynamic Movement Primitive In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pages: 57-64, IEEE, Osaka, Japan, November 2012 (inproceedings)

link (url) DOI [BibTex]

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Learning Force Control Policies for Compliant Robotic Manipulation

Kalakrishnan, M., Righetti, L., Pastor, P., Schaal, S.

In ICML’12 Proceedings of the 29th International Coference on International Conference on Machine Learning, pages: 49-50, Edinburgh, Scotland, 2012 (inproceedings)

[BibTex]

Kalakrishnan, M., Righetti, L., Pastor, P., Schaal, S. Learning Force Control Policies for Compliant Robotic Manipulation In ICML’12 Proceedings of the 29th International Coference on International Conference on Machine Learning, pages: 49-50, Edinburgh, Scotland, 2012 (inproceedings)

[BibTex]

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Quadratic programming for inverse dynamics with optimal distribution of contact forces

Righetti, L., Schaal, S.

In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pages: 538-543, IEEE, Osaka, Japan, November 2012 (inproceedings)

Abstract
In this contribution we propose an inverse dynamics controller for a humanoid robot that exploits torque redundancy to minimize any combination of linear and quadratic costs in the contact forces and the commands. In addition the controller satisfies linear equality and inequality constraints in the contact forces and the commands such as torque limits, unilateral contacts or friction cones limits. The originality of our approach resides in the formulation of the problem as a quadratic program where we only need to solve for the control commands and where the contact forces are optimized implicitly. Furthermore, we do not need a structured representation of the dynamics of the robot (i.e. an explicit computation of the inertia matrix). It is in contrast with existing methods based on quadratic programs. The controller is then robust to uncertainty in the estimation of the dynamics model and the optimization is fast enough to be implemented in high bandwidth torque control loops that are increasingly available on humanoid platforms. We demonstrate properties of our controller with simulations of a human size humanoid robot.

link (url) DOI [BibTex]

Righetti, L., Schaal, S. Quadratic programming for inverse dynamics with optimal distribution of contact forces In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pages: 538-543, IEEE, Osaka, Japan, November 2012 (inproceedings)

link (url) DOI [BibTex]

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Model-free reinforcement learning of impedance control in stochastic environments

Stulp, Freek, Buchli, Jonas, Ellmer, Alice, Mistry, Michael, Theodorou, Evangelos A., Schaal, S.

Autonomous Mental Development, IEEE Transactions on, 4(4):330-341, 2012 (article)

[BibTex]

Stulp, Freek, Buchli, Jonas, Ellmer, Alice, Mistry, Michael, Theodorou, Evangelos A., Schaal, S. Model-free reinforcement learning of impedance control in stochastic environments Autonomous Mental Development, IEEE Transactions on, 4(4):330-341, 2012 (article)

[BibTex]

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Towards Associative Skill Memories

Pastor, P., Kalakrishnan, M., Righetti, L., Schaal, S.

In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pages: 309-315, IEEE, Osaka, Japan, November 2012 (inproceedings)

Abstract
Movement primitives as basis of movement planning and control have become a popular topic in recent years. The key idea of movement primitives is that a rather small set of stereotypical movements should suffice to create a large set of complex manipulation skills. An interesting side effect of stereotypical movement is that it also creates stereotypical sensory events, e.g., in terms of kinesthetic variables, haptic variables, or, if processed appropriately, visual variables. Thus, a movement primitive executed towards a particular object in the environment will associate a large number of sensory variables that are typical for this manipulation skill. These association can be used to increase robustness towards perturbations, and they also allow failure detection and switching towards other behaviors. We call such movement primitives augmented with sensory associations Associative Skill Memories (ASM). This paper addresses how ASMs can be acquired by imitation learning and how they can create robust manipulation skill by determining subsequent ASMs online to achieve a particular manipulation goal. Evaluation for grasping and manipulation with a Barrett WAM/Hand illustrate our approach.

link (url) DOI [BibTex]

Pastor, P., Kalakrishnan, M., Righetti, L., Schaal, S. Towards Associative Skill Memories In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pages: 309-315, IEEE, Osaka, Japan, November 2012 (inproceedings)

link (url) DOI [BibTex]

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Template-based learning of grasp selection

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Asfour, T., Schaal, S.

In 2012 IEEE International Conference on Robotics and Automation, pages: 2379-2384, IEEE, Saint Paul, USA, 2012 (inproceedings)

Abstract
The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects, but still remains an open problem. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects. Moreover, possible configurations also depend on the specific kinematics of the robotic arm and hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time.

link (url) DOI [BibTex]

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Asfour, T., Schaal, S. Template-based learning of grasp selection In 2012 IEEE International Conference on Robotics and Automation, pages: 2379-2384, IEEE, Saint Paul, USA, 2012 (inproceedings)

link (url) DOI [BibTex]

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Reinforcement Learning with Sequences of Motion Primitives for Robust Manipulation

Stulp, F., Theodorou, E., Schaal, S.

IEEE Transactions on Robotics, 2012 (article)

[BibTex]

Stulp, F., Theodorou, E., Schaal, S. Reinforcement Learning with Sequences of Motion Primitives for Robust Manipulation IEEE Transactions on Robotics, 2012 (article)

[BibTex]

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Probabilistic depth image registration incorporating nonvisual information

Wüthrich, M., Pastor, P., Righetti, L., Billard, A., Schaal, S.

In 2012 IEEE International Conference on Robotics and Automation, pages: 3637-3644, IEEE, Saint Paul, USA, 2012 (inproceedings)

Abstract
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL [1] implementations of feature mapping and ICP, especially if nonvisual information is available.

link (url) DOI [BibTex]

Wüthrich, M., Pastor, P., Righetti, L., Billard, A., Schaal, S. Probabilistic depth image registration incorporating nonvisual information In 2012 IEEE International Conference on Robotics and Automation, pages: 3637-3644, IEEE, Saint Paul, USA, 2012 (inproceedings)

link (url) DOI [BibTex]

2011

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Multi-Modal Scene Understanding for Robotic Grasping

Bohg, J.

(2011:17):vi, 194, Trita-CSC-A, KTH Royal Institute of Technology, KTH, Computer Vision and Active Perception, CVAP, Centre for Autonomous Systems, CAS, KTH, Centre for Autonomous Systems, CAS, December 2011 (phdthesis)

Abstract
Current robotics research is largely driven by the vision of creating an intelligent being that can perform dangerous, difficult or unpopular tasks. These can for example be exploring the surface of planet mars or the bottom of the ocean, maintaining a furnace or assembling a car. They can also be more mundane such as cleaning an apartment or fetching groceries. This vision has been pursued since the 1960s when the first robots were built. Some of the tasks mentioned above, especially those in industrial manufacturing, are already frequently performed by robots. Others are still completely out of reach. Especially, household robots are far away from being deployable as general purpose devices. Although advancements have been made in this research area, robots are not yet able to perform household chores robustly in unstructured and open-ended environments given unexpected events and uncertainty in perception and execution.In this thesis, we are analyzing which perceptual and motor capabilities are necessary for the robot to perform common tasks in a household scenario. In that context, an essential capability is to understand the scene that the robot has to interact with. This involves separating objects from the background but also from each other.Once this is achieved, many other tasks become much easier. Configuration of object scan be determined; they can be identified or categorized; their pose can be estimated; free and occupied space in the environment can be outlined.This kind of scene model can then inform grasp planning algorithms to finally pick up objects.However, scene understanding is not a trivial problem and even state-of-the-art methods may fail. Given an incomplete, noisy and potentially erroneously segmented scene model, the questions remain how suitable grasps can be planned and how they can be executed robustly.In this thesis, we propose to equip the robot with a set of prediction mechanisms that allow it to hypothesize about parts of the scene it has not yet observed. Additionally, the robot can also quantify how uncertain it is about this prediction allowing it to plan actions for exploring the scene at specifically uncertain places. We consider multiple modalities including monocular and stereo vision, haptic sensing and information obtained through a human-robot dialog system. We also study several scene representations of different complexity and their applicability to a grasping scenario. Given an improved scene model from this multi-modal exploration, grasps can be inferred for each object hypothesis. Dependent on whether the objects are known, familiar or unknown, different methodologies for grasp inference apply. In this thesis, we propose novel methods for each of these cases. Furthermore,we demonstrate the execution of these grasp both in a closed and open-loop manner showing the effectiveness of the proposed methods in real-world scenarios.

pdf [BibTex]

2011

Bohg, J. Multi-Modal Scene Understanding for Robotic Grasping (2011:17):vi, 194, Trita-CSC-A, KTH Royal Institute of Technology, KTH, Computer Vision and Active Perception, CVAP, Centre for Autonomous Systems, CAS, KTH, Centre for Autonomous Systems, CAS, December 2011 (phdthesis)

pdf [BibTex]

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Mind the gap - robotic grasping under incomplete observation

Bohg, J., Johnson-Roberson, M., Leon, B., Felip, J., Gratal, X., Bergstrom, N., Kragic, D., Morales, A.

In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages: 686-693, May 2011 (inproceedings)

Abstract
We consider the problem of grasp and manipulation planning when the state of the world is only partially observable. Specifically, we address the task of picking up unknown objects from a table top. The proposed approach to object shape prediction aims at closing the knowledge gaps in the robot's understanding of the world. A completed state estimate of the environment can then be provided to a simulator in which stable grasps and collision-free movements are planned. The proposed approach is based on the observation that many objects commonly in use in a service robotic scenario possess symmetries. We search for the optimal parameters of these symmetries given visibility constraints. Once found, the point cloud is completed and a surface mesh reconstructed. Quantitative experiments show that the predictions are valid approximations of the real object shape. By demonstrating the approach on two very different robotic platforms its generality is emphasized.

pdf video code data DOI Project Page [BibTex]

Bohg, J., Johnson-Roberson, M., Leon, B., Felip, J., Gratal, X., Bergstrom, N., Kragic, D., Morales, A. Mind the gap - robotic grasping under incomplete observation In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages: 686-693, May 2011 (inproceedings)

pdf video code data DOI Project Page [BibTex]

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Learning, planning, and control for quadruped locomotion over challenging terrain

Kalakrishnan, Mrinal, Buchli, Jonas, Pastor, Peter, Mistry, Michael, Schaal, S.

International Journal of Robotics Research, 30(2):236-258, February 2011 (article)

[BibTex]

Kalakrishnan, Mrinal, Buchli, Jonas, Pastor, Peter, Mistry, Michael, Schaal, S. Learning, planning, and control for quadruped locomotion over challenging terrain International Journal of Robotics Research, 30(2):236-258, February 2011 (article)

[BibTex]

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STOMP: Stochastic trajectory optimization for motion planning

Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

Abstract
We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on generating noisy trajectories to explore the space around an initial (possibly infeasible) trajectory, which are then combined to produced an updated trajectory with lower cost. A cost function based on a combination of obstacle and smoothness cost is optimized in each iteration. No gradient information is required for the particular optimization algorithm that we use and so general costs for which derivatives may not be available (e.g. costs corresponding to constraints and motor torques) can be included in the cost function. We demonstrate the approach both in simulation and on a dual-arm mobile manipulation system for unconstrained and constrained tasks. We experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based optimizers like CHOMP can get stuck in.

link (url) Project Page [BibTex]

Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S. STOMP: Stochastic trajectory optimization for motion planning In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

link (url) Project Page [BibTex]

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An Experimental Demonstration of a Distributed and Event-based State Estimation Algorithm

(Best Interactive Paper Award (top out of 450))

Trimpe, S., D’Andrea, R.

In Proceedings of the 18th IFAC World Congress, 2011 (inproceedings)

PDF DOI [BibTex]

Trimpe, S., D’Andrea, R. An Experimental Demonstration of a Distributed and Event-based State Estimation Algorithm In Proceedings of the 18th IFAC World Congress, 2011 (inproceedings)

PDF DOI [BibTex]

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Path Integral Control and Bounded Rationality

Braun, D. A., Ortega, P. A., Theodorou, E., Schaal, S.

In IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2011, clmc (inproceedings)

Abstract
Path integral methods [7], [15],[1] have recently been shown to be applicable to a very general class of optimal control problems. Here we examine the path integral formalism from a decision-theoretic point of view, since an optimal controller can always be regarded as an instance of a perfectly rational decision-maker that chooses its actions so as to maximize its expected utility [8]. The problem with perfect rationality is, however, that finding optimal actions is often very difficult due to prohibitive computational resource costs that are not taken into account. In contrast, a bounded rational decision-maker has only limited resources and therefore needs to strike some compromise between the desired utility and the required resource costs [14]. In particular, we suggest an information-theoretic measure of resource costs that can be derived axiomatically [11]. As a consequence we obtain a variational principle for choice probabilities that trades off maximizing a given utility criterion and avoiding resource costs that arise due to deviating from initially given default choice probabilities. The resulting bounded rational policies are in general probabilistic. We show that the solutions found by the path integral formalism are such bounded rational policies. Furthermore, we show that the same formalism generalizes to discrete control problems, leading to linearly solvable bounded rational control policies in the case of Markov systems. Importantly, Bellman?s optimality principle is not presupposed by this variational principle, but it can be derived as a limit case. This suggests that the information- theoretic formalization of bounded rationality might serve as a general principle in control design that unifies a number of recently reported approximate optimal control methods both in the continuous and discrete domain.

PDF [BibTex]

Braun, D. A., Ortega, P. A., Theodorou, E., Schaal, S. Path Integral Control and Bounded Rationality In IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2011, clmc (inproceedings)

PDF [BibTex]

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Skill learning and task outcome prediction for manipulation

Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., Schaal, S.

In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

Abstract
Learning complex motor skills for real world tasks is a hard problem in robotic manipulation that often requires painstaking manual tuning and design by a human expert. In this work, we present a Reinforcement Learning based approach to acquiring new motor skills from demonstration. Our approach allows the robot to learn fine manipulation skills and significantly improve its success rate and skill level starting from a possibly coarse demonstration. Our approach aims to incorporate task domain knowledge, where appropriate, by working in a space consistent with the constraints of a specific task. In addition, we also present an approach to using sensor feedback to learn a predictive model of the task outcome. This allows our system to learn the proprioceptive sensor feedback needed to monitor subsequent executions of the task online and abort execution in the event of predicted failure. We illustrate our approach using two example tasks executed with the PR2 dual-arm robot: a straight and accurate pool stroke and a box flipping task using two chopsticks as tools.

link (url) Project Page Project Page [BibTex]

Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., Schaal, S. Skill learning and task outcome prediction for manipulation In IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011, clmc (inproceedings)

link (url) Project Page Project Page [BibTex]

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An Iterative Path Integral Stochastic Optimal Control Approach for Learning Robotic Tasks

Theodorou, E., Stulp, F., Buchli, J., Schaal, S.

In Proceedings of the 18th World Congress of the International Federation of Automatic Control, 2011, clmc (inproceedings)

Abstract
Recent work on path integral stochastic optimal control theory Theodorou et al. (2010a); Theodorou (2011) has shown promising results in planning and control of nonlinear systems in high dimensional state spaces. The path integral control framework relies on the transformation of the nonlinear Hamilton Jacobi Bellman (HJB) partial differential equation (PDE) into a linear PDE and the approximation of its solution via the use of the Feynman Kac lemma. In this work, we are reviewing the generalized version of path integral stochastic optimal control formalism Theodorou et al. (2010a), used for optimal control and planing of stochastic dynamical systems with state dependent control and diffusion matrices. Moreover we present the iterative path integral control approach, the so called Policy Improvement with Path Integrals or (PI2 ) which is capable of scaling in high dimensional robotic control problems. Furthermore we present a convergence analysis of the proposed algorithm and we apply the proposed framework to a variety of robotic tasks. Finally with the goal to perform locomotion the iterative path integral control is applied for learning nonlinear limit cycle attractors with adjustable land scape.

PDF [BibTex]

Theodorou, E., Stulp, F., Buchli, J., Schaal, S. An Iterative Path Integral Stochastic Optimal Control Approach for Learning Robotic Tasks In Proceedings of the 18th World Congress of the International Federation of Automatic Control, 2011, clmc (inproceedings)

PDF [BibTex]

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Enhanced visual scene understanding through human-robot dialog

Johnson-Roberson, M., Bohg, J., Skantze, G., Gustafson, J., Carlson, R., Rasolzadeh, B., Kragic, D.

In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages: 3342-3348, 2011 (inproceedings)

Abstract
We propose a novel human-robot-interaction framework for robust visual scene understanding. Without any a-priori knowledge about the objects, the task of the robot is to correctly enumerate how many of them are in the scene and segment them from the background. Our approach builds on top of state-of-the-art computer vision methods, generating object hypotheses through segmentation. This process is combined with a natural dialog system, thus including a `human in the loop' where, by exploiting the natural conversation of an advanced dialog system, the robot gains knowledge about ambiguous situations. We present an entropy-based system allowing the robot to detect the poorest object hypotheses and query the user for arbitration. Based on the information obtained from the human-robot dialog, the scene segmentation can be re-seeded and thereby improved. We present experimental results on real data that show an improved segmentation performance compared to segmentation without interaction.

pdf video DOI Project Page [BibTex]

Johnson-Roberson, M., Bohg, J., Skantze, G., Gustafson, J., Carlson, R., Rasolzadeh, B., Kragic, D. Enhanced visual scene understanding through human-robot dialog In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages: 3342-3348, 2011 (inproceedings)

pdf video DOI Project Page [BibTex]

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Risk and gain battery management for self-docking mobile robots

Berenz, V., Suzuki, K.

In Robotics and Biomimetics (ROBIO), 2011 IEEE International Conference on, pages: 1766-1771, 2011 (inproceedings)

DOI [BibTex]

Berenz, V., Suzuki, K. Risk and gain battery management for self-docking mobile robots In Robotics and Biomimetics (ROBIO), 2011 IEEE International Conference on, pages: 1766-1771, 2011 (inproceedings)

DOI [BibTex]

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Reduced Communication State Estimation for Control of an Unstable Networked Control System

Trimpe, S., D’Andrea, R.

In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011 (inproceedings)

PDF Supplementary material DOI [BibTex]

Trimpe, S., D’Andrea, R. Reduced Communication State Estimation for Control of an Unstable Networked Control System In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011 (inproceedings)

PDF Supplementary material DOI [BibTex]

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Neuromuscular Stochastic Optimal Control of a Tendon Driven Index Finger

Theodorou, E. A., Todorov, E., Valero-Cuevas, F.

In Proceedings of American Control Conference (ACC), 2011, clmc (inproceedings)

Abstract
With the goal to build robotic hands which can reach the levels of dexterity and robustness of the hand, the question of what are the candidate control principles that can handle the nonlinearities, the high dimensionality and the internal noise of biomechanical structures of the complexity of the hand, is still open. In this work we present the first stochastic optimal feedback controller applied to a full tendon driven simulated robotic index finger. In our model we do take into account the full tendon structure of the index finger which consist of 11 tendons based on the underlying physiology and we consider muscle with the typical force - length and force velocity properties. Our feedback controller show robustness against noise and perturbation of the dynamics while it can also successfully handle the nonlinearities and high dimensionality of the robotic index finger. Furthermore as it is shown in the evaluations, it provides the complete time history of the tendon excursions and the tendon velocities of the index finger for the tasks of tapping with zero and nonzero terminal velocities.

PDF [BibTex]

Theodorou, E. A., Todorov, E., Valero-Cuevas, F. Neuromuscular Stochastic Optimal Control of a Tendon Driven Index Finger In Proceedings of American Control Conference (ACC), 2011, clmc (inproceedings)

PDF [BibTex]

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Bayesian robot system identification with input and output noise

Ting, J., D’Souza, A., Schaal, S.

Neural Networks, 24(1):99-108, 2011, clmc (article)

Abstract
For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods

link (url) [BibTex]

Ting, J., D’Souza, A., Schaal, S. Bayesian robot system identification with input and output noise Neural Networks, 24(1):99-108, 2011, clmc (article)

link (url) [BibTex]

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TDM: A software framework for elegant and rapid development of autonomous behaviors for humanoid robots.

Berenz, V., Tanaka, F., Suzuki, K., Herink, M.

In Humanoids, pages: 179-186, IEEE, 2011 (inproceedings)

link (url) [BibTex]

Berenz, V., Tanaka, F., Suzuki, K., Herink, M. TDM: A software framework for elegant and rapid development of autonomous behaviors for humanoid robots. In Humanoids, pages: 179-186, IEEE, 2011 (inproceedings)

link (url) [BibTex]

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Coaching robot behavior using continuous physiological affective feedback

Gruebler, A., Berenz, V., Suzuki, K.

In 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011), Bled, Slovenia, October 26-28, 2011, pages: 466-471, 2011 (inproceedings)

link (url) DOI [BibTex]

Gruebler, A., Berenz, V., Suzuki, K. Coaching robot behavior using continuous physiological affective feedback In 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011), Bled, Slovenia, October 26-28, 2011, pages: 466-471, 2011 (inproceedings)

link (url) DOI [BibTex]

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Learning variable impedance control

Buchli, J., Stulp, F., Theodorou, E., Schaal, S.

International Journal of Robotics Research, 2011, clmc (article)

Abstract
One of the hallmarks of the performance, versatility, and robustness of biological motor control is the ability to adapt the impedance of the overall biomechanical system to different task requirements and stochastic disturbances. A transfer of this principle to robotics is desirable, for instance to enable robots to work robustly and safely in everyday human environments. It is, however, not trivial to derive variable impedance controllers for practical high degree-of-freedom (DOF) robotic tasks. In this contribution, we accomplish such variable impedance control with the reinforcement learning (RL) algorithm PISq ({f P}olicy {f I}mprovement with {f P}ath {f I}ntegrals). PISq is a model-free, sampling based learning method derived from first principles of stochastic optimal control. The PISq algorithm requires no tuning of algorithmic parameters besides the exploration noise. The designer can thus fully focus on cost function design to specify the task. From the viewpoint of robotics, a particular useful property of PISq is that it can scale to problems of many DOFs, so that reinforcement learning on real robotic systems becomes feasible. We sketch the PISq algorithm and its theoretical properties, and how it is applied to gain scheduling for variable impedance control. We evaluate our approach by presenting results on several simulated and real robots. We consider tasks involving accurate tracking through via-points, and manipulation tasks requiring physical contact with the environment. In these tasks, the optimal strategy requires both tuning of a reference trajectory emph{and} the impedance of the end-effector. The results show that we can use path integral based reinforcement learning not only for planning but also to derive variable gain feedback controllers in realistic scenarios. Thus, the power of variable impedance control is made available to a wide variety of robotic systems and practical applications.

link (url) [BibTex]

Buchli, J., Stulp, F., Theodorou, E., Schaal, S. Learning variable impedance control International Journal of Robotics Research, 2011, clmc (article)

link (url) [BibTex]

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Iterative path integral stochastic optimal control: Theory and applications to motor control

Theodorou, E. A.

University of Southern California, University of Southern California, Los Angeles, CA, 2011 (phdthesis)

PDF [BibTex]

Theodorou, E. A. Iterative path integral stochastic optimal control: Theory and applications to motor control University of Southern California, University of Southern California, Los Angeles, CA, 2011 (phdthesis)

PDF [BibTex]

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Learning of grasp selection based on shape-templates

Herzog, A.

Karlsruhe Institute of Technology, 2011 (mastersthesis)

[BibTex]

Herzog, A. Learning of grasp selection based on shape-templates Karlsruhe Institute of Technology, 2011 (mastersthesis)

[BibTex]

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Understanding haptics by evolving mechatronic systems

Loeb, G. E., Tsianos, G.A., Fishel, J.A., Wettels, N., Schaal, S.

Progress in Brain Research, 192, pages: 129, 2011 (article)

[BibTex]

Loeb, G. E., Tsianos, G.A., Fishel, J.A., Wettels, N., Schaal, S. Understanding haptics by evolving mechatronic systems Progress in Brain Research, 192, pages: 129, 2011 (article)

[BibTex]

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Movement segmentation using a primitive library

Meier, F., Theodorou, E., Stulp, F., Schaal, S.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), Sept. 25-30, San Francisco, CA, 2011, clmc (inproceedings)

Abstract
Segmenting complex movements into a sequence of primitives remains a difficult problem with many applications in the robotics and vision communities. In this work, we show how the movement segmentation problem can be reduced to a sequential movement recognition problem. To this end, we reformulate the orig-inal Dynamic Movement Primitive (DMP) formulation as a linear dynamical sys-tem with control inputs. Based on this new formulation, we develop an Expecta-tion-Maximization algorithm to estimate the duration and goal position of a par-tially observed trajectory. With the help of this algorithm and the assumption that a library of movement primitives is present, we present a movement seg-mentation framework. We illustrate the usefulness of the new DMP formulation on the two applications of online movement recognition and movement segmen-tation.

link (url) [BibTex]

Meier, F., Theodorou, E., Stulp, F., Schaal, S. Movement segmentation using a primitive library In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), Sept. 25-30, San Francisco, CA, 2011, clmc (inproceedings)

link (url) [BibTex]

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Learning Force Control Policies for Compliant Manipulation

Kalakrishnan, M., Righetti, L., Pastor, P., Schaal, S.

In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 4639-4644, IEEE, San Francisco, USA, sep 2011 (inproceedings)

Abstract
Developing robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manipulation tasks imply complex contact interactions with the external world, and involve reasoning about the forces and torques to be applied. Planning under contact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learning. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI2) algorithm to learn these force/torque profiles by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.

link (url) DOI [BibTex]

Kalakrishnan, M., Righetti, L., Pastor, P., Schaal, S. Learning Force Control Policies for Compliant Manipulation In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 4639-4644, IEEE, San Francisco, USA, sep 2011 (inproceedings)

link (url) DOI [BibTex]

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Control of legged robots with optimal distribution of contact forces

Righetti, L., Buchli, J., Mistry, M., Schaal, S.

In 2011 11th IEEE-RAS International Conference on Humanoid Robots, pages: 318-324, IEEE, Bled, Slovenia, 2011 (inproceedings)

Abstract
The development of agile and safe humanoid robots require controllers that guarantee both high tracking performance and compliance with the environment. More specifically, the control of contact interaction is of crucial importance for robots that will actively interact with their environment. Model-based controllers such as inverse dynamics or operational space control are very appealing as they offer both high tracking performance and compliance. However, while widely used for fully actuated systems such as manipulators, they are not yet standard controllers for legged robots such as humanoids. Indeed such robots are fundamentally different from manipulators as they are underactuated due to their floating-base and subject to switching contact constraints. In this paper we present an inverse dynamics controller for legged robots that use torque redundancy to create an optimal distribution of contact constraints. The resulting controller is able to minimize, given a desired motion, any quadratic cost of the contact constraints at each instant of time. In particular we show how this can be used to minimize tangential forces during locomotion, therefore significantly improving the locomotion of legged robots on difficult terrains. In addition to the theoretical result, we present simulations of a humanoid and a quadruped robot, as well as experiments on a real quadruped robot that demonstrate the advantages of the controller.

link (url) DOI [BibTex]

Righetti, L., Buchli, J., Mistry, M., Schaal, S. Control of legged robots with optimal distribution of contact forces In 2011 11th IEEE-RAS International Conference on Humanoid Robots, pages: 318-324, IEEE, Bled, Slovenia, 2011 (inproceedings)

link (url) DOI [BibTex]

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Learning Motion Primitive Goals for Robust Manipulation

Stulp, F., Theodorou, E., Kalakrishnan, M., Pastor, P., Righetti, L., Schaal, S.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 325-331, IEEE, San Francisco, USA, sep 2011 (inproceedings)

Abstract
Applying model-free reinforcement learning to manipulation remains challenging for several reasons. First, manipulation involves physical contact, which causes discontinuous cost functions. Second, in manipulation, the end-point of the movement must be chosen carefully, as it represents a grasp which must be adapted to the pose and shape of the object. Finally, there is uncertainty in the object pose, and even the most carefully planned movement may fail if the object is not at the expected position. To address these challenges we 1) present a simplified, computationally more efficient version of our model-free reinforcement learning algorithm PI2; 2) extend PI2 so that it simultaneously learns shape parameters and goal parameters of motion primitives; 3) use shape and goal learning to acquire motion primitives that are robust to object pose uncertainty. We evaluate these contributions on a manipulation platform consisting of a 7-DOF arm with a 4-DOF hand.

link (url) DOI [BibTex]

Stulp, F., Theodorou, E., Kalakrishnan, M., Pastor, P., Righetti, L., Schaal, S. Learning Motion Primitive Goals for Robust Manipulation In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 325-331, IEEE, San Francisco, USA, sep 2011 (inproceedings)

link (url) DOI [BibTex]

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Inverse Dynamics Control of Floating-Base Robots with External Constraints: a Unified View

Righetti, L., Buchli, J., Mistry, M., Schaal, S.

In 2011 IEEE International Conference on Robotics and Automation, pages: 1085-1090, IEEE, Shanghai, China, 2011 (inproceedings)

Abstract
Inverse dynamics controllers and operational space controllers have proved to be very efficient for compliant control of fully actuated robots such as fixed base manipulators. However legged robots such as humanoids are inherently different as they are underactuated and subject to switching external contact constraints. Recently several methods have been proposed to create inverse dynamics controllers and operational space controllers for these robots. In an attempt to compare these different approaches, we develop a general framework for inverse dynamics control and show that these methods lead to very similar controllers. We are then able to greatly simplify recent whole-body controllers based on operational space approaches using kinematic projections, bringing them closer to efficient practical implementations. We also generalize these controllers such that they can be optimal under an arbitrary quadratic cost in the commands.

link (url) DOI [BibTex]

Righetti, L., Buchli, J., Mistry, M., Schaal, S. Inverse Dynamics Control of Floating-Base Robots with External Constraints: a Unified View In 2011 IEEE International Conference on Robotics and Automation, pages: 1085-1090, IEEE, Shanghai, China, 2011 (inproceedings)

link (url) DOI [BibTex]

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Online movement adaptation based on previous sensor experiences

Pastor, P., Righetti, L., Kalakrishnan, M., Schaal, S.

In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 365-371, IEEE, San Francisco, USA, sep 2011 (inproceedings)

Abstract
Personal robots can only become widespread if they are capable of safely operating among humans. In uncertain and highly dynamic environments such as human households, robots need to be able to instantly adapt their behavior to unforseen events. In this paper, we propose a general framework to achieve very contact-reactive motions for robotic grasping and manipulation. Associating stereotypical movements to particular tasks enables our system to use previous sensor experiences as a predictive model for subsequent task executions. We use dynamical systems, named Dynamic Movement Primitives (DMPs), to learn goal-directed behaviors from demonstration. We exploit their dynamic properties by coupling them with the measured and predicted sensor traces. This feedback loop allows for online adaptation of the movement plan. Our system can create a rich set of possible motions that account for external perturbations and perception uncertainty to generate truly robust behaviors. As an example, we present an application to grasping with the WAM robot arm.

link (url) DOI [BibTex]

Pastor, P., Righetti, L., Kalakrishnan, M., Schaal, S. Online movement adaptation based on previous sensor experiences In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 365-371, IEEE, San Francisco, USA, sep 2011 (inproceedings)

link (url) DOI [BibTex]

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Learning to grasp under uncertainty

Stulp, F., Theodorou, E., Buchli, J., Schaal, S.

In Robotics and Automation (ICRA), 2011 IEEE International Conference on, Shanghai, China, May 9-13, 2011, clmc (inproceedings)

Abstract
We present an approach that enables robots to learn motion primitives that are robust towards state estimation uncertainties. During reaching and preshaping, the robot learns to use fine manipulation strategies to maneuver the object into a pose at which closing the hand to perform the grasp is more likely to succeed. In contrast, common assumptions in grasp planning and motion planning for reaching are that these tasks can be performed independently, and that the robot has perfect knowledge of the pose of the objects in the environment. We implement our approach using Dynamic Movement Primitives and the probabilistic model-free reinforcement learning algorithm Policy Improvement with Path Integrals (PI2 ). The cost function that PI2 optimizes is a simple boolean that penalizes failed grasps. The key to acquiring robust motion primitives is to sample the actual pose of the object from a distribution that represents the state estimation uncertainty. During learning, the robot will thus optimize the chance of grasping an object from this distribution, rather than at one specific pose. In our empirical evaluation, we demonstrate how the motion primitives become more robust when grasping simple cylindrical objects, as well as more complex, non-convex objects. We also investigate how well the learned motion primitives generalize towards new object positions and other state estimation uncertainty distributions.

link (url) [BibTex]

Stulp, F., Theodorou, E., Buchli, J., Schaal, S. Learning to grasp under uncertainty In Robotics and Automation (ICRA), 2011 IEEE International Conference on, Shanghai, China, May 9-13, 2011, clmc (inproceedings)

link (url) [BibTex]

2002

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Forward models in visuomotor control

Mehta, B., Schaal, S.

J Neurophysiol, 88(2):942-53, August 2002, clmc (article)

Abstract
In recent years, an increasing number of research projects investigated whether the central nervous system employs internal models in motor control. While inverse models in the control loop can be identified more readily in both motor behavior and the firing of single neurons, providing direct evidence for the existence of forward models is more complicated. In this paper, we will discuss such an identification of forward models in the context of the visuomotor control of an unstable dynamic system, the balancing of a pole on a finger. Pole balancing imposes stringent constraints on the biological controller, as it needs to cope with the large delays of visual information processing while keeping the pole at an unstable equilibrium. We hypothesize various model-based and non-model-based control schemes of how visuomotor control can be accomplished in this task, including Smith Predictors, predictors with Kalman filters, tapped-delay line control, and delay-uncompensated control. Behavioral experiments with human participants allow exclusion of most of the hypothesized control schemes. In the end, our data support the existence of a forward model in the sensory preprocessing loop of control. As an important part of our research, we will provide a discussion of when and how forward models can be identified and also the possible pitfalls in the search for forward models in control.

link (url) [BibTex]

2002

Mehta, B., Schaal, S. Forward models in visuomotor control J Neurophysiol, 88(2):942-53, August 2002, clmc (article)

link (url) [BibTex]