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检索条件"机构=Computational Learning and Motor Control Laboratory"

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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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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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Probabilistic depth image registration incorporating nonvisual information

Probabilistic depth image registration incorporating nonvisu...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Manuel Wüthrich Peter Pastor Ludovic Righetti Aude Billard Stefan Schaal Faculty of Micro Engineering EPF Lausanne Switzerland Computational Learning and Motor Control Laboratory (CLMC) USC USA Learning Algorithms and Systems Laboratory (LASA) EPF Lausanne Switzerland

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.

关键词： Iterative closest point algorithm Cameras Robots Shape Covariance matrix Visualization Approximation algorithms

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learning force control policies for compliant manipulation

Learning force control policies for compliant manipulation

引用

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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

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 (PI 2 ) 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.

关键词： Trajectory Force Robots Cost function Noise Torque Joints

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Hierarchical reinforcement learning with movement primitives

Hierarchical reinforcement learning with movement primitives

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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 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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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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Reinforcement learning of impedance control in stochastic force fields

Reinforcement learning of impedance control in stochastic fo...

引用

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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