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IEEE-RAS International Conference on Humanoid Robots

作者： Freek Stulp Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA

Temporal abstraction and task decomposition drastically reduce the search space for planning and control, and are fundamental to making complex tasks amenable to learning. In the context of reinforcement learning, temporal abstractions are studied within the paradigm of hierarchical reinforcement learning. We propose a hierarchical reinforcement learning approach by applying our algorithm PI 2 to sequences of Dynamic Movement Primitives. For robots, this representation has some important advantages over discrete representations in terms of scalability and convergence speed. The parameters of the Dynamic Movement Primitives are learned simultaneously at different levels of temporal abstraction. The shape of a movement primitive is optimized w.r.t. the costs up to the next primitive in the sequence, and the subgoals between two movement primitives w.r.t. the costs up to the end of the entire movement primitive sequence. We implement our approach on an 11-DOF arm and hand, and evaluate it in a pick-and-place task in which the robot transports an object between different shelves in a cupboard.

关键词： Shape Trajectory learning Robots Aerospace electronics Noise Cost function

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learning to Grasp under Uncertainty

Learning to Grasp under Uncertainty

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Freek Stulp Evangelos Theodorou Jonas Buchli Stefan Schaal Computational Learning and Motor Control Lab University of Southern CaliforniaLos AngelesCA 90089

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.

关键词： Uncertainty Trajectory Robots State estimation Grasping Robustness Planning

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Compliant quadruped locomotion over rough terrain

Compliant quadruped locomotion over rough terrain

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2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Jonas Buchli Mrinal Kalakrishnan Michael Mistry Peter Pastor Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA

Many critical elements for statically stable walking for legged robots have been known for a long time, including stability criteria based on support polygons, good foothold selection, recovery strategies to name a few. All these criteria have to be accounted for in the planning as well as the control phase. Most legged robots usually employ high gain position control, which means that it is crucially important that the planned reference trajectories are a good match for the actual terrain, and that tracking is accurate. Such an approach leads to conservative controllers, i.e. relatively low speed, ground speed matching, etc. Not surprisingly such controllers are not very robust - they are not suited for the real world use outside of the laboratory where the knowledge of the world is limited and error prone. Thus, to achieve robust robotic locomotion in the archetypical domain of legged systems, namely complex rough terrain, where the size of the obstacles are in the order of leg length, additional elements are required. A possible solution to improve the robustness of legged locomotion is to maximize the compliance of the controller. While compliance is trivially achieved by reduced feedback gains, for terrain requiring precise foot placement (e.g. climbing rocks, walking over pegs or cracks) compliance cannot be introduced at the cost of inferior tracking. Thus, model-based control and - in contrast to passive dynamic walkers - active balance control is required. To achieve these objectives, in this paper we add two crucial elements to legged locomotion, i.e., floating-base inverse dynamics control and predictive force control, and we show that these elements increase robustness in face of unknown and unanticipated perturbations (e.g. obstacles). Furthermore, we introduce a novel line-based COG trajectory planner, which yields a simpler algorithm than traditional polygon based methods and creates the appropriate input to our control *** show results from bot

关键词： Legged locomotion Force control Robust control Robustness Stability criteria Position control Trajectory Laboratories Error correction Leg

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Editorial

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International Journal of Humanoid Robotics 2005年第4期2卷 389-390页

作者： CHENG, GORDON SCHAAL, STEFAN ATKESON, CHRISTOPHER G. JST-ICORP Computational Brain Project ATR Computational Neuroscience Laboratory 2-2-2 Keihanna Science City Soraku-gun Kyoto619-0288 Japan Computational Learning and Motor Control Lab University of Southern California Hedco Neurosciences Building HNB-103 3641 Watt Way Los AngelesCA90089-2520 United States Robotics Institute Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States

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Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization

Virtual vs. real: Trading off simulations and physical exper...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Marco, Alonso Berkenkamp, Felix Hennig, Philipp Schoellig, Angela P. Krause, Andreas Schaal, Stefan Trimpe, Sebastian Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zurich Switzerland Canada Computational Learning and Motor Control Lab University of Southern California United States Max Planck ETH Center for Learning Systems Tubingen Germany Zurich Switzerland

ISBN: (纸本)9781509046331

In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to automate this process, yet often requires too many experiments to be practical. In this paper, we propose a solution to this problem by exploiting prior knowledge from simulations, which are readily available for most robotic platforms. Specifically, we extend Entropy Search, a Bayesian optimization algorithm that maximizes information gain from each experiment, to the case of multiple information sources. The result is a principled way to automatically combine cheap, but inaccurate information from simulations with expensive and accurate physical experiments in a cost-effective manner. We apply the resulting method to a cart-pole system, which confirms that the algorithm can find good control policies with fewer experiments than standard Bayesian optimization on the physical system only. © 2017 IEEE.

关键词： Reinforcement learning

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Compact models of motor primitive variations for predictable reaching and obstacle avoidance

Compact models of motor primitive variations for predictable...

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9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09

作者： Stulp, Freek Oztop, Erhan Pastor, Peter Beetz, Michael Schaaz, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA United States Kyoto Japan Computational Neuroscience Laboratories Advanced Telecommunications Research Institute International Kyoto Japan Intelligent Autonomous Systems Group Technische Universitdt Munchen Munich Germany

ISBN: (纸本)9781424445882

In most activities of daily living, related tasks are encountered over and over again. This regularity allows humans and robots to reuse existing solutions for known recurring tasks. We expect that reusing a set of standard solutions to solve similar tasks will facilitate the design and on-line adaptation of the control systems of robots operating in human environments. In this paper, we derive a set of standard solutions for reaching behavior from human motion data. We also derive stereotypical reaching trajectories for variations of the task, in which obstacles are present. These stereotypical trajectories are then compactly represented with Dynamic Movement Primitives. On the humanoid robot Sarcos CB, this approach leads to reproducible, predictable, and human-like reaching motions. ©2009 IEEE.

关键词： Anthropomorphic robots

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learning motion primitive goals for robust manipulation

Learning motion primitive goals for robust manipulation

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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Freek Stulp Evangelos Theodorou Mrinal Kalakrishnan Peter Pastor Ludovic Righetti Stefan Schaal Computational Learning and Motor Control laboratory University of Southern California Los Angeles CA USA

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 PI 2 ; 2) extend PI 2 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.

关键词： Shape Trajectory Uncertainty Robots Cost function learning Grasping

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Movement segmentation using a primitive library

Movement segmentation using a primitive library

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2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Franziska Meier Evangelos Theodorou Freek Stulp Stefan Schaal Computational Learning and Motor Control laboratory University of Southern California Los Angeles CA USA

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 original Dynamic Movement Primitive (DMP) formulation as a linear dynamical system with control inputs. Based on this new formulation, we develop an Expectation-Maximization algorithm to estimate the duration and goal position of a partially observed trajectory. With the help of this algorithm and the assumption that a library of movement primitives is present, we present a movement segmentation framework. We illustrate the usefulness of the new DMP formulation on the two applications of online movement recognition and movement segmentation.

关键词： Trajectory Libraries Motion segmentation Covariance matrix Noise Equations

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An Iterative Path Integral Stochastic Optimal control Approach for learning Robotic Tasks

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IFAC Proceedings Volumes 2011年第1期44卷 11594-11601页

作者： Evangelos Theodorou Freek Stulp Jonas Buchli Stefan Schaal Computational Learning and Motor Control Lab University of Southern California USA Department of Advanced Robotics Italian Institute of Technology ATR Computational Neuroscience Laboratories Kyoto 619-0288 Japan

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 P olicy I mprovement with P ath I ntegrals or (PI 2 ) 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.

关键词： Path Integrals Stochastic Optimal control Robotics

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Movement segmentation and recognition for imitation learning 15

Movement segmentation and recognition for imitation learning

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15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012

作者： Meier, Franziska Theodorou, Evangelos Schaal, Stefan Computational Learning and Motor Control Lab University of Southern California Los Angeles United States Department of Computer Science and Engineering University of Washington Seattle United States Max-Planck-Institute for Intelligent Systems Tübingen Germany

In human movement learning, it is most common to teach constituent elements of complex movements in isolation, before chaining them into complex movements. Segmentation and recognition of observed movement could thus proceed out of this existing knowledge, which is directly compatible with movement generation. In this paper, we address exactly this scenario. We assume that a library of movement primitives has already been taught, and we wish to identify elements of the library in a complex motor act, where the individual elements have been smoothed together, and, occasionally, there might be a movement segment that is not in our library yet. We employ a flexible machine learning representation of movement primitives based on learnable nonlinear attractor system. For the purpose of movement segmentation and recognition, it is possible to reformulate this representation as a controlled linear dynamical system. An Expectation-Maximization algorithm can be developed to estimate the open parameters of a movement primitive from the library, using as input an observed trajectory piece. If no matching primitive from the library can be found, a new primitive is created. This process allows a straightforward sequential segmentation of observed movement into known and new primitives, which are suitable for robot imitation learning. We illustrate our approach with synthetic examples and data collected from human movement.

关键词： Maximum principle

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