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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Manuel Wuthrich Peter Pastor Mrinal Kalakrishnan Jeannette Bohg Stefan Schaal Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems Tubingen Germany Computational Learning and Motor Control lab at the University of Southern California Los Angeles CA USA

ISBN: (纸本)9781467363563

We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compute a posterior distribution over the current object pose. Depending on whether a robot or a human manipulates the object, we employ a process model with or without knowledge of control inputs. Observations are obtained from a range camera. As opposed to previous object tracking methods, we explicitly model self-occlusions and occlusions from the environment, e.g, the human or robotic hand. This leads to a strongly non-linear observation model and additional dependencies in the Bayesian network. We employ a Rao-Blackwellised particle filter to compute an estimate of the object pose at every time step. In a set of experiments, we demonstrate the ability of our method to accurately and robustly track the object pose in real-time while it is being manipulated by a human or a robot.

关键词： Probabilistic Object Tracking Range Camera 6-DoF

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Towards Associative Skill Memories

Towards Associative Skill Memories

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IEEE-RAS International Conference on Humanoid Robots

作者： Peter Pastor Mrinal Kalakrishnan Ludovic Righetti Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Angeles USA

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.

关键词： Robot sensing systems Force Fingers Torque Trajectory Uncertainty

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Quadratic programming for inverse dynamics with optimal distribution of contact forces

Quadratic programming for inverse dynamics with optimal dist...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ludovic Righetti Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Angeles USA

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.

关键词： Dynamics Robots Acceleration Force Equations Torque Foot

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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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Encoding of periodic and their transient motions by a single dynamic movement primitive

Encoding of periodic and their transient motions by a single...

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IEEE-RAS International Conference on Humanoid Robots

作者： Johannes Ernesti Ludovic Righetti Martin Do Tamim Asfour Stefan Schaal Karlsruher Institut fur Technologie Karlsruhe Baden-WÃ¼rttemberg DE Computational Learning and Motor Control Lab University of Southern California Los Angeles California Institute for Anthropomatics Karlsruhe Institute of Technology Germany

Present formulations of periodic dynamic movement primitives (DMPs) do not encode the transient behavior required to start the rhythmic motion, although these transient movements are an important part of the rhythmic movements (i.e. when walking, there is always a first step that is very different from the subsequent ones). An ad-hoc procedure is then necessary to get the robot into the periodic motion. In this contribution we present a novel representation for rhythmic Dynamic Movement Primitives (DMPs) that encodes both the rhythmic motion and its transient behaviors. As with previously proposed DMPs, we use a dynamical system approach where an asymptotically stable limit cycle represents the periodic pattern. Transients are then represented as trajectories converging towards the limit cycle, different trajectories representing varying transients from different initial conditions. Our approach thus constitutes a generalization of previously proposed rhythmic DMPs. Experiments conducted on the humanoid robot ARMAR-III demonstrate the applicability of the approach for movement generation.

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Hierarchical reinforcement learning with movement primitives

Hierarchical reinforcement learning with movement primitives

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2011 11th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS 2011

作者： Stulp, Freek Schaal, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA 90089 United States

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, tem... 详细信息

ISBN: (纸本)9781612848679

关键词： Reinforcement learning

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An iterative path integral stochastic optimal control approach for learning robotic tasks

An iterative path integral stochastic optimal control approa...

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

ISBN: (纸本)9783902661937

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 (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. © 2011 IFAC.

关键词： Iterative methods

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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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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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control of legged robots with optimal distribution of contact forces

Control of legged robots with optimal distribution of contac...

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2011 11th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS 2011

作者： Righetti, Ludovic Buchli, Jonas Mistry, Michael Schaal, Stefan Computational Learning and Motor Control Lab. University of Southern California Los Angeles CA 90089 United States Max Planck Institute for Intelligent Systems Tübingen Germany Dept. of Advanced Robotics Italian Institute of Technology Genoa Italy Disney Research Pittsburgh Pittsburgh PA 15213 United States

ISBN: (纸本)9781612848679

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. © 2011 IEEE.

关键词： controllers

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