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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jeannette Bohg Javier Romero Alexander Herzog Stefan Schaal Autonomous Motion Department Perceiving Systems Max-Planck-Institute for Intelligent Systems Tübingen Germany Computational Learning and Motor Control lab University of Southern California Los Angeles CA USA

We propose to frame the problem of marker-less robot arm pose estimation as a pixel-wise part classification problem. As input, we use a depth image in which each pixel is classified to be either from a particular robot part or the background. The classifier is a random decision forest trained on a large number of synthetically generated and labeled depth images. From all the training samples ending up at a leaf node, a set of offsets is learned that votes for relative joint positions. Pooling these votes over all foreground pixels and subsequent clustering gives us an estimate of the true joint positions. Due to the intrinsic parallelism of pixel-wise classification, this approach can run in super real-time and is more efficient than previous ICP-like methods. We quantitatively evaluate the accuracy of this approach on synthetic data. We also demonstrate that the method produces accurate joint estimates on real data despite being purely trained on synthetic data.

关键词： Joints Three-dimensional displays Robot sensing systems Estimation Training data Kinematics

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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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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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Combining learned and analytical models for predicting action effects from sensory data

arXiv

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arXiv 2017年

作者： Kloss, Alina Schaal, Stefan Bohg, Jeannette Autonomous Motion Department Max Planck Institute for Intelligent Systems Germany Computational Learning and Motor Control Lab University of Southern California United States Department of Computer Science Stanford University United States

One of the most basic skills a robot should possess is predicting the effect of physical interactions with objects in the environment. This enables optimal action selection to reach a certain goal state. Traditionally, dynamics are approximated by physics-based analytical models. These models rely on specific state representations that may be hard to obtain from raw sensory data, especially if no knowledge of the object shape is assumed. More recently, we have seen learning approaches that can predict the effect of complex physical interactions directly from sensory input. It is however an open question how far these models generalize beyond their training data. In this work, we investigate the advantages and limitations of neural network based learning approaches for predicting the effects of actions based on sensory input and show how analytical and learned models can be combined to leverage the best of both worlds. As physical interaction task, we use planar pushing, for which there exists a well-known analytical model and a large realworld dataset. We propose to use a convolutional neural network to convert raw depth images or organized point clouds into a suitable representation for the analytical model and compare this approach to using neural networks for both, perception and prediction. A systematic evaluation of the proposed approach on a very large real-world dataset shows two main advantages of the hybrid architecture. Compared to a pure neural network, it significantly (i) reduces required training data and (ii) improves generalization to novel physical interaction. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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

arXiv

引用

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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