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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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Drifting Gaussian processes with varying neighborhood sizes for online model learning

Drifting Gaussian processes with varying neighborhood sizes ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Franziska Meier Stefan Schaal Max-Planck-Institute for Intelligent Systems Tübingen Germany Computational Learning and Motor Control Lab University of Southern California Los Angeles CA USA University of Southern California Los Angeles CA US

ISBN: (纸本)9781467380270

computationally efficient online learning of non-stationary models remains a difficult challenge. A robust and reliable algorithm could have great impact on problems in learning control. Recent work on combining the worlds of computationally efficient and locally adaptive learning algorithms with robust learning frameworks such as Gaussian process regression has taken a step towards both robust and real-time capable learning systems. However, online learning of model parameters on streaming data - that is strongly correlated, such as data arriving along a trajectory - can still create serious issues for many learning systems. Here we investigate the idea of drifting Gaussian processes which explicitly exploit the fact that data is generated along trajectories. A drifting Gaussian process keeps a history of a constant number of recently observed data points and updates its hyper-parameters at each time step. Instead of optimizing the neighborhood size on which the GP is trained on, we propose to use several - in parallel - drifting GPs whose predictions are combined for query points. We illustrate our approach on synthetically generated data and successfully evaluate on inverse dynamics learning tasks.

关键词： Gaussian processes Adaptation models Data models computational modeling Ground penetrating radar Kernel Trajectory

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Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement learning with Bayesian Optimization

arXiv

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arXiv 2017年

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 Tübingen Germany Max Planck ETH Center for Learning Systems Zürich Switzerland

— 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. Copyright © 2017, The Authors. All rights reserved.

关键词： Reinforcement learning

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Robot arm pose estimation by pixel-wise regression of joint angles

Robot arm pose estimation by pixel-wise regression of joint ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Felix Widmaier Daniel Kappler Stefan Schaal Jeannette Bohg Autonomous Motion Department Max-Planck-Institute for Intelligent Systems Tübingen Germany Karls University of Tübingen Germany Computational Learning and Motor Control lab at the University of Southern California Los Angeles CA USA

ISBN: (纸本)9781467380270

To achieve accurate vision-based control with a robotic arm, a good hand-eye coordination is required. However, knowing the current configuration of the arm can be very difficult due to noisy readings from joint encoders or an inaccurate hand-eye calibration. We propose an approach for robot arm pose estimation that uses depth images of the arm as input to directly estimate angular joint positions. This is a frame-by-frame method which does not rely on good initialisation of the solution from the previous frames or knowledge from the joint encoders. For estimation, we employ a random regression forest which is trained on synthetically generated data. We compare different training objectives of the forest and also analyse the influence of prior segmentation of the arms on accuracy. We show that this approach improves previous work both in terms of computational complexity and accuracy. Despite being trained on synthetic data only, we demonstrate that the estimation also works on real depth images.

关键词： Training Radio frequency Impurities Estimation Robot kinematics Measurement

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State Estimation for a Humanoid Robot

State Estimation for a Humanoid Robot

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Nicholas Rotella Michael Bloesch Ludovic Righetti Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Angeles California Autonomous Systems Lab ETH Zurich Zurich Switzerland Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

ISBN: (纸本)9781479969357

This paper introduces a framework for state estimation on a humanoid robot platform using only common proprioceptive sensors and knowledge of leg kinematics. The presented approach extends that detailed in prior work on a point-foot quadruped platform by adding the rotational constraints imposed by the humanoid's flat feet. As in previous work, the proposed Extended Kalman Filter accommodates contact switching and makes no assumptions about gait or terrain, making it applicable on any humanoid platform for use in any task. A nonlinear observability analysis is performed on both the point-foot and flat-foot filters and it is concluded that the addition of rotational constraints significantly simplifies singular cases and improves the observability characteristics of the system. Results on a simulated walking dataset demonstrate the performance gain of the flat-foot filter as well as confirm the results of the presented observability analysis.

关键词： Humanoid robots State estimation Observability Platform Flatfoot

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Surface tilt perception with a biomimetic tactile sensor

Surface tilt perception with a biomimetic tactile sensor

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Zhe Su Stefan Schaal Gerald E. Loeb Computational Learning and Motor Control Lab University of Southern California Los Angeles CA USA Autonomous Motion Department Max-Planck Institute for Intelligent Systems Tubingen Germany SynTouch LLC Los Angeles CA USA

ISBN: (纸本)9781509032884

Humans are known to be good at manipulating tools. To cope with disturbances and uncertainties from the external environment during such tasks, they must be able to perceive small changes in orientation or tilt of the tool using mechanoreceptors in the glabrous skin of the fingertips. We hypothesize that the most sensitive part of human fingers, a flat surface on the distal phalanx (called apical tuft) would be preferred for perceiving very fine tilts. In this paper, we used an experimental apparatus to quantify discrimination threshold of a biomimetic tactile sensor (BioTac®) that incorporates a similar, sensorized flat surface. We found the thresholds to be as small as 0.11° for tilts in the roll direction and 0.19° for tilts in the pitch direction. The flat surface was superior in detecting tilts when compared to other, curved locations on the BioTac.

关键词： Electrodes Skin Force Fingers Tactile sensors

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Model-based policy search for automatic tuning of multivariate PID controllers

Model-based policy search for automatic tuning of multivaria...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Andreas Doerr Duy Nguyen-Tuong Alonso Marco Stefan Schaal Sebastian Trimpe Autonomous Motion Department at the Max Planck Institute for Intelligent Systems Tübingen Germany Bosch Center for Artificial Intelligence Renningen Germany Computational Learning and Motor Control lab at the University of Southern California Los Angeles CA USA

PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely based on data observed on an otherwise unknown system. The system's state is extended appropriately to frame the PID policy as a static state feedback policy. This renders PID tuning possible as the solution of a finite horizon optimal control problem without further a priori knowledge. The framework is applied to the task of balancing an inverted pendulum on a seven degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast and data-efficient policy learning, even on complex real world problems.

关键词： Tuning PD control PI control computational modeling Data models State feedback Process control

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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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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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Model-based policy search for automatic tuning of multivariate pid controllers

arXiv

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arXiv 2017年

作者： Doerr, Andreas Nguyen-Tuong, Duy Marco, Alonso Schaal, Stefan Trimpe, Sebastian Bosch Center for Artificial Intelligence Renningen Germany Autonomous Motion Department Max Planck Institute for Intelligent Systems Tübingen Germany Computational Learning and Motor Control lab University of Southern California Los AngelesCA United States

关键词： Proportional control systems

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