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IEEE International Conference on Robotics and Automation (ICRA)

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

We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization and control techniques to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal foothold choices from expert demonstration using terrain templates, (2) a body trajectory optimizer based on the Zero-Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller by testing it on the LittleDog quadruped robot, over a wide variety of rough terrain of varying difficulty levels. We demonstrate the generalization ability of this controller by presenting test results from an independent external test team on terrains that have never been shown to us.

关键词： Robustness Legged locomotion Force control Foot Leg Size control Robust control Mobile robots Software testing Optimal control

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Trajectory formation for imitation with nonlinear dynamical systems

Trajectory formation for imitation with nonlinear dynamical ...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： A.J. Ijspeert J. Nakanishi S. Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Kawato Dynamic Brain Project ATR Kyoto Japan

Explores an approach to learning by imitation and trajectory formation by representing movements as mixtures of nonlinear differential equations with well-defined attractor dynamics. An observed movement is approximated by finding a best fit of the mixture model to its data by a recursive least squares regression technique. In contrast to non-autonomous movement representations like splines, the resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy which is robust to strong external perturbations and that can be modified by additional perceptual variables. This movement policy remains the same for a given target, regardless of the initial conditions, and can easily be re-used for new targets. We evaluate the trajectory formation system in the context of a humanoid robot simulation that is part of the Virtual Trainer project, which aims at supervising rehabilitation exercises in stroke-patients. A typical rehabilitation exercise was collected with a Sarcos Sensuit, a device to record joint angular movement from human subjects, and approximated and reproduced with our imitation techniques. Our results demonstrate that multijoint human movements can be encoded successfully, and that this system allows robust modifications of the,movement policy through external variables.

关键词： Nonlinear dynamical systems control systems Least squares approximation Humanoid robots Robustness Spline Neural networks motor drives Laboratories Trajectory

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Movement imitation with nonlinear dynamical systems in humanoid robots

Movement imitation with nonlinear dynamical systems in human...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： A.J. Ijspeert J. Nakanishi S. Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA ATR Human Information Systems Laboratories Kyoto Japan

Presents an approach to movement planning, on-line trajectory modification, and imitation learning by representing movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics. The resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy (CP) which is robust to strong external perturbations and that can be modified on-line by additional perceptual variables. We evaluate the system with a humanoid robot simulation and an actual humanoid robot. Experiments are presented for the imitation of three types of movements: reaching movements with one arm, drawing movements of 2-D patterns, and tennis swings. Our results demonstrate (a) that multi-joint human movements can be encoded successfully by the CPs, (b) that a learned movement policy can readily be reused to produce robust trajectories towards different targets, (c) that a policy fitted for one particular target provides a good predictor of human reaching movements towards neighboring targets, and (d) that the parameter space which encodes a policy is suitable for measuring to which extent two trajectories are qualitatively similar.

关键词： Nonlinear dynamical systems Humanoid robots Humans Robustness Laboratories Convergence Trajectory Encoding control systems Biological system modeling

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Toward simple control for complex, autonomous robotic applications: Combining discrete and rhythmic motor primitives

Toward simple control for complex, autonomous robotic applic...

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作者： Degallier, Sarah Righetti, Ludovic Gay, Sebastien Ijspeert, Auke CNBI Laboratory School of Engineering EPFL Ecole Polytechnique Fédérale de Lausanne Lausanne 1015 Switzerland Biorobotics Laboratory School of Engineering EPFL Ecole Polytechnique Fédérale de Lausanne Lausanne 1015 Switzerland Computational Learning and Motor Control Lab Computer Science Neurosciences and Biomedical Engineering University of Southern California Los Angeles CA 90089 United States

Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching). © 2011 Springer Science+Business Media, LLC.

关键词： Anthropomorphic robots

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Inverse kinematics for humanoid robots

Inverse kinematics for humanoid robots

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IEEE International Conference on Robotics and Automation (ICRA)

作者： G. Tevatia S. Schaal Kawato Dynamic Brain Project ERATO Japan Science and Technology Agency Kyoto Japan Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA

Real-time control of the end-effector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates methods of resolved motion rate control (RMRC) that employ optimization criteria to resolve kinematic redundancies. In particular we focus on two established techniques, the pseudo inverse with explicit optimization and the extended Jacobian method. We prove that the extended Jacobian method includes pseudo-inverse methods as a special solution. In terms of computational complexity, however pseudo-inverse and extended Jacobian differ significantly in favor of pseudo-inverse methods. Employing numerical estimation techniques, we introduce a computationally efficient version of the extended Jacobian with performance comparable to the original version. Our results are illustrated in simulation studies with a multiple degree-of-freedom robot, and were tested on a 30 degree-of-freedom humanoid robot.

关键词： Humanoid robots Jacobian matrices Robot kinematics Optimization methods Manipulators Motion control Orbital robotics Actuators Null space motor drives

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Reinforcement learning of impedance control in stochastic force fields

Reinforcement learning of impedance control in stochastic fo...

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IEEE International Conference on Development and learning, ICDL

作者： Freek Stulp Jonas Buchli Alice Ellmer Michael Mistry Evangelos Theodorou Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Department of Advanced Robotics Italian Institute of Technology Genova Italy Disney Research Pittsburgh PA USA

Variable impedance control is essential for ensuring robust and safe physical interaction with the environment. As demonstrated in numerous force field experiments, humans combine two strategies to adapt their impedance to external perturbations: 1) if perturbations are unpredictable, subjects increase their impedance through co-contraction; 2) if perturbations are predictable, subjects learn a feed-forward command to counter the known perturbation. In this paper, we apply the force field paradigm to a simulated 7-DOF robot, by exerting stochastic forces on the robot's end-effector. The robot `subject' uses our model-free reinforcement learning algorithm PI2 to simultaneously learn the end-effector trajectories and variable impedance schedules. We demonstrate how the robot learns the same two-fold strategy to perturbation rejection as humans do, resulting in qualitatively similar behavior. Our results provide a biologically plausible approach to learning appropriate impedances purely from experience, without requiring a model of either body or environment dynamics.

关键词： computational modeling Noise measurement

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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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IEEE-RAS International Conference on Humanoid Robots

作者： Ludovic Righetti Jonas Buchli Michael Mistry Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Department of Advanced Robotics Italian Institute of Technology Genova Italy Disney Research Pittsburgh Pittsburgh PA USA

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.

关键词： Torque Dynamics Acceleration Redundancy Legged locomotion Joints

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learning task error models for manipulation

Learning task error models for manipulation

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Peter Pastor Mrinal Kalakrishnan Jonathan Binney Jonathan Kelly Ludovic Righetti Gaurav Sukhatme Stefan Schaal Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Robotic Embedded Systems Laboratory University of Southern California Los Angeles CA USA University of Southern California Los Angeles CA US

Precise kinematic forward models are important for robots to successfully perform dexterous grasping and manipulation tasks, especially when visual servoing is rendered infeasible due to occlusions. A lot of research has been conducted to estimate geometric and non-geometric parameters of kinematic chains to minimize reconstruction errors. However, kinematic chains can include non-linearities, e.g. due to cable stretch and motor-side encoders, that result in significantly different errors for different parts of the state space. Previous work either does not consider such non-linearities or proposes to estimate non-geometric parameters of carefully engineered models that are robot specific. We propose a data-driven approach that learns task error models that account for such unmodeled non-linearities. We argue that in the context of grasping and manipulation, it is sufficient to achieve high accuracy in the task relevant state space. We identify this relevant state space using previously executed joint configurations and learn error corrections for those. Therefore, our system is developed to generate subsequent executions that are similar to previous ones. The experiments show that our method successfully captures the non-linearities in the head kinematic chain (due to a counterbalancing spring) and the arm kinematic chains (due to cable stretch) of the considered experimental platform, see Fig. 1. The feasibility of the presented error learning approach has also been evaluated in independent DARPA ARM-S testing contributing to successfully complete 67 out of 72 grasping and manipulation tasks.

关键词： Kinematics Joints Cameras Robots Training computational modeling Grasping

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learning movement primitives

Springer Tracts in Advanced Robotics

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Springer Tracts in Advanced Robotics 2005年 15卷 561-572页

作者： Schaal, Stefan Peters, Jan Nakanishi, Jun Ijspeert, Auke Computational Learning and Motor Control Laboratory Computer Science and Neuroscience University of Southern California Los Angeles CA 90089-2520 United States Dept of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratory 2-2-2 Hikaridai Seika-cho Soraku-gun 619-0288 Kyoto Japan School of Computer and Communication Sciences EPFL Swiss Federal Institute of Technology Lausanne CH 1015 Lausanne Switzerland

This paper discusses a comprehensive framework for modular motor control based on a recently developed theory of dynamic movement primitives (DMP). DMPs are a formulation of movement primitives with autonomous nonlinear differential equations, whose time evolution creates smooth kinematic control policies. Model-based control theory is used to convert the outputs of these policies into motor commands. By means of coupling terms, on-line modifications can be incorporated into the time evolution of the differential equations, thus providing a rather flexible and reactive framework for motor planning and execution. The linear parameterization of DMPs lends itself naturally to supervised learning from demonstration. Moreover, the temporal, scale, and translation invariance of the differential equations with respect to these parameters provides a useful means for movement recognition. A novel reinforcement learning technique based on natural stochastic policy gradients allows a general approach of improving DMPs by trial and error learning with respect to almost arbitrary optimization criteria. We demonstrate the different ingredients of the DMP approach in various examples, involving skill learning from demonstration on the humanoid robot DB, and learning biped walking from demonstration in simulation, including self-improvement of the movement patterns towards energy efficiency through resonance tuning. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Differential equations

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Hypothesis testing framework for active object detection

Hypothesis testing framework for active object detection

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Nikolay Atanasov Bharath Sankaran Jerome Le Ny Thomas Koletschka George J. Pappas Kostas Daniilidis Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA Computational Learning and Motor Control Laboratory University of Southern California Los Angeles CA USA Department of Electrical Engineering Ecole Polytechnique de Montreal QUE Canada Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781467356404

One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection by controlling the point of view of a mobile depth camera. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. The sensor then plans a sequence of viewpoints, which balances the amount of energy used to move with the chance of identifying the correct hypothesis. We formulate an active M-ary hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate POMDP algorithm. The validity of our approach is verified through simulation and experiments with real scenes captured by a kinect sensor. The results suggest a significant improvement over static object detection.

关键词： Vocabulary Feature extraction Mobile communication Object detection Robot sensing systems Planning Optimization

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