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56 results (View BibTeX file of all listed publications)

2012

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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]

2012

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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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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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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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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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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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]

2011

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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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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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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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]

2005

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Composite adaptive control with locally weighted statistical learning

Nakanishi, J., Farrell, J. A., Schaal, S.

Neural Networks, 18(1):71-90, January 2005, clmc (article)

Abstract
This paper introduces a provably stable learning adaptive control framework with statistical learning. The proposed algorithm employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the proposed learning adaptive control algorithm uses both the tracking error and the estimation error to update the parameters. We first discuss statistical learning of nonlinear functions, and motivate our choice of the locally weighted learning framework. Second, we begin with a class of first order SISO systems for theoretical development of our learning adaptive control framework, and present a stability proof including a parameter projection method that is needed to avoid potential singularities during adaptation. Then, we generalize our adaptive controller to higher order SISO systems, and discuss further extension to MIMO problems. Finally, we evaluate our theoretical control framework in numerical simulations to illustrate the effectiveness of the proposed learning adaptive controller for rapid convergence and high accuracy of control.

link (url) [BibTex]

2005

Nakanishi, J., Farrell, J. A., Schaal, S. Composite adaptive control with locally weighted statistical learning Neural Networks, 18(1):71-90, January 2005, clmc (article)

link (url) [BibTex]

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Natural Actor-Critic

Peters, J., Vijayakumar, S., Schaal, S.

In Proceedings of the 16th European Conference on Machine Learning, 3720, pages: 280-291, (Editors: Gama, J.;Camacho, R.;Brazdil, P.;Jorge, A.;Torgo, L.), Springer, ECML, 2005, clmc (inproceedings)

Abstract
This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing AmariÕs natural gradient approach, while the critic obtains both the natural policy gradient and additional parameters of a value function simultaneously by linear regres- sion. We show that actor improvements with natural policy gradients are particularly appealing as these are independent of coordinate frame of the chosen policy representation, and can be estimated more efficiently than regular policy gradients. The critic makes use of a special basis function parameterization motivated by the policy-gradient compatible function approximation. We show that several well-known reinforcement learning methods such as the original Actor-Critic and BradtkeÕs Linear Quadratic Q-Learning are in fact Natural Actor-Critic algorithms. Em- pirical evaluations illustrate the effectiveness of our techniques in com- parison to previous methods, and also demonstrate their applicability for learning control on an anthropomorphic robot arm.

link (url) DOI [BibTex]

Peters, J., Vijayakumar, S., Schaal, S. Natural Actor-Critic In Proceedings of the 16th European Conference on Machine Learning, 3720, pages: 280-291, (Editors: Gama, J.;Camacho, R.;Brazdil, P.;Jorge, A.;Torgo, L.), Springer, ECML, 2005, clmc (inproceedings)

link (url) DOI [BibTex]

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Comparative experiments on task space control with redundancy resolution

Nakanishi, J., Cory, R., Mistry, M., Peters, J., Schaal, S.

In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 3901-3908, Edmonton, Alberta, Canada, Aug. 2-6, IROS, 2005, clmc (inproceedings)

Abstract
Understanding the principles of motor coordination with redundant degrees of freedom still remains a challenging problem, particularly for new research in highly redundant robots like humanoids. Even after more than a decade of research, task space control with redundacy resolution still remains an incompletely understood theoretical topic, and also lacks a larger body of thorough experimental investigation on complex robotic systems. This paper presents our first steps towards the development of a working redundancy resolution algorithm which is robust against modeling errors and unforeseen disturbances arising from contact forces. To gain a better understanding of the pros and cons of different approaches to redundancy resolution, we focus on a comparative empirical evaluation. First, we review several redundancy resolution schemes at the velocity, acceleration and torque levels presented in the literature in a common notational framework and also introduce some new variants of these previous approaches. Second, we present experimental comparisons of these approaches on a seven-degree-of-freedom anthropomorphic robot arm. Surprisingly, one of our simplest algorithms empirically demonstrates the best performance, despite, from a theoretical point, the algorithm does not share the same beauty as some of the other methods. Finally, we discuss practical properties of these control algorithms, particularly in light of inevitable modeling errors of the robot dynamics.

link (url) DOI [BibTex]

Nakanishi, J., Cory, R., Mistry, M., Peters, J., Schaal, S. Comparative experiments on task space control with redundancy resolution In Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 3901-3908, Edmonton, Alberta, Canada, Aug. 2-6, IROS, 2005, clmc (inproceedings)

link (url) DOI [BibTex]

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A model of smooth pursuit based on learning of the target dynamics using only retinal signals

Shibata, T., Tabata, H., Schaal, S., Kawato, M.

Neural Networks, 18, pages: 213-225, 2005, clmc (article)

Abstract
While the predictive nature of the primate smooth pursuit system has been evident through several behavioural and neurophysiological experiments, few models have attempted to explain these results comprehensively. The model we propose in this paper in line with previous models employing optimal control theory; however, we hypothesize two new issues: (1) the medical superior temporal (MST) area in the cerebral cortex implements a recurrent neural network (RNN) in order to predict the current or future target velocity, and (2) a forward model of the target motion is acquired by on-line learning. We use stimulation studies to demonstrate how our new model supports these hypotheses.

link (url) [BibTex]

Shibata, T., Tabata, H., Schaal, S., Kawato, M. A model of smooth pursuit based on learning of the target dynamics using only retinal signals Neural Networks, 18, pages: 213-225, 2005, clmc (article)

link (url) [BibTex]

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Linear and Nonlinear Estimation models applied to Hemodynamic Model

Theodorou, E.

Technical Report-2005-1, Computational Action and Vision Lab University of Minnesota, 2005, clmc (techreport)

Abstract
The relation between BOLD signal and neural activity is still poorly understood. The Gaussian Linear Model known as GLM is broadly used in many fMRI data analysis for recovering the underlying neural activity. Although GLM has been proved to be a really useful tool for analyzing fMRI data it can not be used for describing the complex biophysical process of neural metabolism. In this technical report we make use of a system of Stochastic Differential Equations that is based on Buxton model [1] for describing the underlying computational principles of hemodynamic process. Based on this SDE we built a Kalman Filter estimator so as to estimate the induced neural signal as well as the blood inflow under physiologic and sensor noise. The performance of Kalman Filter estimator is investigated under different physiologic noise characteristics and measurement frequencies.

PDF [BibTex]

Theodorou, E. Linear and Nonlinear Estimation models applied to Hemodynamic Model Technical Report-2005-1, Computational Action and Vision Lab University of Minnesota, 2005, clmc (techreport)

PDF [BibTex]

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Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares

Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., Schaal, S.

In Advances in Neural Information Processing Systems 18 (NIPS 2005), (Editors: Weiss, Y.;Schölkopf, B.;Platt, J.), Cambridge, MA: MIT Press, Vancouver, BC, Dec. 6-11, 2005, clmc (inproceedings)

Abstract
An increasing number of projects in neuroscience requires the statistical analysis of high dimensional data sets, as, for instance, in predicting behavior from neural firing, or in operating artificial devices from brain recordings in brain-machine interfaces. Linear analysis techniques remain prevalent in such cases, but classi-cal linear regression approaches are often numercially too fragile in high dimen-sions. In this paper, we address the question of whether EMG data collected from arm movements of monkeys can be faithfully reconstructed with linear ap-proaches from neural activity in primary motor cortex (M1). To achieve robust data analysis, we develop a full Bayesian approach to linear regression that automatically detects and excludes irrelevant features in the data, and regular-izes against overfitting. In comparison with ordinary least squares, stepwise re-gression, partial least squares, and a brute force combinatorial search for the most predictive input features in the data, we demonstrate that the new Bayesian method offers a superior mixture of characteristics in terms of regularization against overfitting, computational efficiency, and ease of use, demonstrating its potential as a drop-in replacement for other linear regression techniques. As neuroscientific results, our analyses demonstrate that EMG data can be well pre-dicted from M1 neurons, further opening the path for possible real-time inter-faces between brains and machines.

link (url) [BibTex]

Ting, J., D’Souza, A., Yamamoto, K., Yoshioka, T., Hoffman, D., Kakei, S., Sergio, L., Kalaska, J., Kawato, M., Strick, P., Schaal, S. Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares In Advances in Neural Information Processing Systems 18 (NIPS 2005), (Editors: Weiss, Y.;Schölkopf, B.;Platt, J.), Cambridge, MA: MIT Press, Vancouver, BC, Dec. 6-11, 2005, clmc (inproceedings)

link (url) [BibTex]

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Rapbid synchronization and accurate phase-locking of rhythmic motor primitives

Pongas, D., Billard, A., Schaal, S.

In IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pages: 2911-2916, Edmonton, Alberta, Canada, Aug. 2-6, 2005, clmc (inproceedings)

Abstract
Rhythmic movement is ubiquitous in human and animal behavior, e.g., as in locomotion, dancing, swimming, chewing, scratching, music playing, etc. A particular feature of rhythmic movement in biology is the rapid synchronization and phase locking with other rhythmic events in the environment, for instance music or visual stimuli as in ball juggling. In traditional oscillator theories to rhythmic movement generation, synchronization with another signal is relatively slow, and it is not easy to achieve accurate phase locking with a particular feature of the driving stimulus. Using a recently developed framework of dynamic motor primitives, we demonstrate a novel algorithm for very rapid synchronizaton of a rhythmic movement pattern, which can phase lock any feature of the movement to any particulur event in the driving stimulus. As an example application, we demonstrate how an anthropomorphic robot can use imitation learning to acquire a complex rumming pattern and keep it synchronized with an external rhythm generator that changes its frequency over time.

link (url) [BibTex]

Pongas, D., Billard, A., Schaal, S. Rapbid synchronization and accurate phase-locking of rhythmic motor primitives In IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pages: 2911-2916, Edmonton, Alberta, Canada, Aug. 2-6, 2005, clmc (inproceedings)

link (url) [BibTex]

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Parametric and Non-Parametric approaches for nonlinear tracking of moving objects

Hidaka, Y, Theodorou, E.

Technical Report-2005-1, 2005, clmc (article)

PDF [BibTex]

Hidaka, Y, Theodorou, E. Parametric and Non-Parametric approaches for nonlinear tracking of moving objects Technical Report-2005-1, 2005, clmc (article)

PDF [BibTex]

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A new methodology for robot control design

Peters, J., Mistry, M., Udwadia, F. E., Schaal, S.

In The 5th ASME International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC 2005), Long Beach, CA, Sept. 24-28, 2005, clmc (inproceedings)

Abstract
Gauss principle of least constraint and its generalizations have provided a useful insights for the development of tracking controllers for mechanical systems (Udwadia,2003). Using this concept, we present a novel methodology for the design of a specific class of robot controllers. With our new framework, we demonstrate that well-known and also several novel nonlinear robot control laws can be derived from this generic framework, and show experimental verifications on a Sarcos Master Arm robot for some of these controllers. We believe that the suggested approach unifies and simplifies the design of optimal nonlinear control laws for robots obeying rigid body dynamics equations, both with or without external constraints, holonomic or nonholonomic constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.

link (url) [BibTex]

Peters, J., Mistry, M., Udwadia, F. E., Schaal, S. A new methodology for robot control design In The 5th ASME International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC 2005), Long Beach, CA, Sept. 24-28, 2005, clmc (inproceedings)

link (url) [BibTex]

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Arm movement experiments with joint space force fields using an exoskeleton robot

Mistry, M., Mohajerian, P., Schaal, S.

In IEEE Ninth International Conference on Rehabilitation Robotics, pages: 408-413, Chicago, Illinois, June 28-July 1, 2005, clmc (inproceedings)

Abstract
A new experimental platform permits us to study a novel variety of issues of human motor control, particularly full 3-D movements involving the major seven degrees-of-freedom (DOF) of the human arm. We incorporate a seven DOF robot exoskeleton, and can minimize weight and inertia through gravity, Coriolis, and inertia compensation, such that subjects' arm movements are largely unaffected by the manipulandum. Torque perturbations can be individually applied to any or all seven joints of the human arm, thus creating novel dynamic environments, or force fields, for subjects to respond and adapt to. Our first study investigates a joint space force field where the shoulder velocity drives a disturbing force in the elbow joint. Results demonstrate that subjects learn to compensate for the force field within about 100 trials, and from the strong presence of aftereffects when removing the field in some randomized catch trials, that an inverse dynamics, or internal model, of the force field is formed by the nervous system. Interestingly, while post-learning hand trajectories return to baseline, joint space trajectories remained changed in response to the field, indicating that besides learning a model of the force field, the nervous system also chose to exploit the space to minimize the effects of the force field on the realization of the endpoint trajectory plan. Further applications for our apparatus include studies in motor system redundancy resolution and inverse kinematics, as well as rehabilitation.

link (url) [BibTex]

Mistry, M., Mohajerian, P., Schaal, S. Arm movement experiments with joint space force fields using an exoskeleton robot In IEEE Ninth International Conference on Rehabilitation Robotics, pages: 408-413, Chicago, Illinois, June 28-July 1, 2005, clmc (inproceedings)

link (url) [BibTex]

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A unifying framework for the control of robotics systems

Peters, J., Mistry, M., Udwadia, F. E., Cory, R., Nakanishi, J., Schaal, S.

In IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pages: 1824-1831, Edmonton, Alberta, Canada, Aug. 2-6, 2005, clmc (inproceedings)

Abstract
Recently, [1] suggested to derive tracking controllers for mechanical systems using a generalization of GaussÕ principle of least constraint. This method al-lows us to reformulate control problems as a special class of optimal control. We take this line of reasoning one step further and demonstrate that well-known and also several novel nonlinear robot control laws can be derived from this generic methodology. We show experimental verifications on a Sar-cos Master Arm robot for some of the the derived controllers.We believe that the suggested approach offers a promising unification and simplification of nonlinear control law design for robots obeying rigid body dynamics equa-tions, both with or without external constraints, with over-actuation or under-actuation, as well as open-chain and closed-chain kinematics.

link (url) [BibTex]

Peters, J., Mistry, M., Udwadia, F. E., Cory, R., Nakanishi, J., Schaal, S. A unifying framework for the control of robotics systems In IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pages: 1824-1831, Edmonton, Alberta, Canada, Aug. 2-6, 2005, clmc (inproceedings)

link (url) [BibTex]

1999

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Is imitation learning the route to humanoid robots?

Schaal, S.

Trends in Cognitive Sciences, 3(6):233-242, 1999, clmc (article)

Abstract
This review will focus on two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It will be postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. This hope is justified because imitation learning channels research efforts towards three important issues: efficient motor learning, the connection between action and perception, and modular motor control in form of movement primitives. In order to make these points, first, a brief review of imitation learning will be given from the view of psychology and neuroscience. In these fields, representations and functional connections between action and perception have been explored that contribute to the understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution provided a first idea of the possible neural basis of imitation. Secondly, computational approaches to imitation learning will be described, initially from the perspective of traditional AI and robotics, and then with a focus on neural network models and statistical learning research. Parallels and differences between biological and computational approaches to imitation will be highlighted. The review will end with an overview of current projects that actually employ imitation learning for humanoid robots.

link (url) [BibTex]

1999

Schaal, S. Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences, 3(6):233-242, 1999, clmc (article)

link (url) [BibTex]

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Nonparametric regression for learning nonlinear transformations

Schaal, S.

In Prerational Intelligence in Strategies, High-Level Processes and Collective Behavior, 2, pages: 595-621, (Editors: Ritter, H.;Cruse, H.;Dean, J.), Kluwer Academic Publishers, 1999, clmc (inbook)

Abstract
Information processing in animals and artificial movement systems consists of a series of transformations that map sensory signals to intermediate representations, and finally to motor commands. Given the physical and neuroanatomical differences between individuals and the need for plasticity during development, it is highly likely that such transformations are learned rather than pre-programmed by evolution. Such self-organizing processes, capable of discovering nonlinear dependencies between different groups of signals, are one essential part of prerational intelligence. While neural network algorithms seem to be the natural choice when searching for solutions for learning transformations, this paper will take a more careful look at which types of neural networks are actually suited for the requirements of an autonomous learning system. The approach that we will pursue is guided by recent developments in learning theory that have linked neural network learning to well established statistical theories. In particular, this new statistical understanding has given rise to the development of neural network systems that are directly based on statistical methods. One family of such methods stems from nonparametric regression. This paper will compare nonparametric learning with the more widely used parametric counterparts in a non technical fashion, and investigate how these two families differ in their properties and their applicabilities. We will argue that nonparametric neural networks offer a set of characteristics that make them a very promising candidate for on-line learning in autonomous system.

link (url) [BibTex]

Schaal, S. Nonparametric regression for learning nonlinear transformations In Prerational Intelligence in Strategies, High-Level Processes and Collective Behavior, 2, pages: 595-621, (Editors: Ritter, H.;Cruse, H.;Dean, J.), Kluwer Academic Publishers, 1999, clmc (inbook)

link (url) [BibTex]

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Segmentation of endpoint trajectories does not imply segmented control

Sternad, D., Schaal, D.

Experimental Brain Research, 124(1):118-136, 1999, clmc (article)

Abstract
While it is generally assumed that complex movements consist of a sequence of simpler units, the quest to define these units of action, or movement primitives, still remains an open question. In this context, two hypotheses of movement segmentation of endpoint trajectories in 3D human drawing movements are re-examined: (1) the stroke-based segmentation hypothesis based on the results that the proportionality coefficient of the 2/3 power law changes discontinuously with each new â??strokeâ?, and (2) the segmentation hypothesis inferred from the observation of piecewise planar endpoint trajectories of 3D drawing movements. In two experiments human subjects performed a set of elliptical and figure-8 patterns of different sizes and orientations using their whole arm in 3D. The kinematic characteristics of the endpoint trajectories and the seven joint angles of the arm were analyzed. While the endpoint trajectories produced similar segmentation features as reported in the literature, analyses of the joint angles show no obvious segmentation but rather continuous oscillatory patterns. By approximating the joint angle data of human subjects with sinusoidal trajectories, and by implementing this model on a 7-degree-of-freedom anthropomorphic robot arm, it is shown that such a continuous movement strategy can produce exactly the same features as observed by the above segmentation hypotheses. The origin of this apparent segmentation of endpoint trajectories is traced back to the nonlinear transformations of the forward kinematics of human arms. The presented results demonstrate that principles of discrete movement generation may not be reconciled with those of rhythmic movement as easily as has been previously suggested, while the generalization of nonlinear pattern generators to arm movements can offer an interesting alternative to approach the question of units of action.

link (url) [BibTex]

Sternad, D., Schaal, D. Segmentation of endpoint trajectories does not imply segmented control Experimental Brain Research, 124(1):118-136, 1999, clmc (article)

link (url) [BibTex]

1993

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Learning passive motor control strategies with genetic algorithms

Schaal, S., Sternad, D.

In 1992 Lectures in complex systems, pages: 913-918, (Editors: Nadel, L.;Stein, D.), Addison-Wesley, Redwood City, CA, 1993, clmc (inbook)

Abstract
This study investigates learning passive motor control strategies. Passive control is understood as control without active error correction; the movement is stabilized by particular properties of the controlling dynamics. We analyze the task of juggling a ball on a racket. An approximation to the optimal solution of the task is derived by means of optimization theory. In order to model the learning process, the problem is coded for a genetic algorithm in representations without sensory or with sensory information. For all representations the genetic algorithm is able to find passive control strategies, but learning speed and the quality of the outcome are significantly different. A comparison with data from human subjects shows that humans seem to apply yet different movement strategies to the ones proposed. For the feedback representation some implications arise for learning from demonstration.

link (url) [BibTex]

1993

Schaal, S., Sternad, D. Learning passive motor control strategies with genetic algorithms In 1992 Lectures in complex systems, pages: 913-918, (Editors: Nadel, L.;Stein, D.), Addison-Wesley, Redwood City, CA, 1993, clmc (inbook)

link (url) [BibTex]

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A genetic algorithm for evolution from an ecological perspective

Sternad, D., Schaal, S.

In 1992 Lectures in Complex Systems, pages: 223-231, (Editors: Nadel, L.;Stein, D.), Addison-Wesley, Redwood City, CA, 1993, clmc (inbook)

Abstract
In the population model presented, an evolutionary dynamic is explored which is based on the operator characteristics of genetic algorithms. An essential modification in the genetic algorithms is the inclusion of a constraint in the mixing of the gene pool. The pairing for the crossover is governed by a selection principle based on a complementarity criterion derived from the theoretical tenet of perception-action (P-A) mutuality of ecological psychology. According to Swenson and Turvey [37] P-A mutuality underlies evolution and is an integral part of its thermodynamics. The present simulation tested the contribution of P-A-cycles in evolutionary dynamics. A numerical experiment compares the population's evolution with and without this intentional component. The effect is measured in the difference of the rate of energy dissipation, as well as in three operationalized aspects of complexity. The results support the predicted increase in the rate of energy dissipation, paralleled by an increase in the average heterogeneity of the population. Furthermore, the spatio-temporal evolution of the system is tested for the characteristic power-law relations of a nonlinear system poised in a critical state. The frequency distribution of consecutive increases in population size shows a significantly different exponent in functional relationship.

[BibTex]

Sternad, D., Schaal, S. A genetic algorithm for evolution from an ecological perspective In 1992 Lectures in Complex Systems, pages: 223-231, (Editors: Nadel, L.;Stein, D.), Addison-Wesley, Redwood City, CA, 1993, clmc (inbook)

[BibTex]

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Roles for memory-based learning in robotics

Atkeson, C. G., Schaal, S.

In Proceedings of the Sixth International Symposium on Robotics Research, pages: 503-521, Hidden Valley, PA, 1993, clmc (inproceedings)

[BibTex]

Atkeson, C. G., Schaal, S. Roles for memory-based learning in robotics In Proceedings of the Sixth International Symposium on Robotics Research, pages: 503-521, Hidden Valley, PA, 1993, clmc (inproceedings)

[BibTex]