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

共 70 条记录，以下是21-30 订阅

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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 ...

引用

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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Automatic LQR tuning based on Gaussian process global optimization

Automatic LQR tuning based on Gaussian process global optimi...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Alonso Marco Philipp Hennig Jeannette Bohg Stefan Schaal Sebastian Trimpe Max Planck Institute for Intelligent Systems Tübingen Germany Max-Planck-Institut fur Intelligente Systeme Stuttgart Baden-WÃ¼rttemberg DE Computational Learning and Motor Control Lab at the University of Southern California Los Angeles CA USA

ISBN: (纸本)9781467380270

This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of two- and four-dimensional tuning problems highlight the method's potential for automatic controller tuning on robotic platforms.

关键词： Tuning computational modeling Robots Bayes methods Cost function Gaussian processes

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Leveraging big data for grasp planning

Leveraging big data for grasp planning

引用

IEEE International Conference on Robotics and Automation (ICRA)

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

We propose a new large-scale database containing grasps that are applied to a large set of objects from numerous categories. These grasps are generated in simulation and are annotated with different grasp stability metrics. We use a descriptive and efficient representation of the local object shape at which each grasp is applied. Given this data, we present a two-fold analysis: (i) We use crowdsourcing to analyze the correlation of the metrics with grasp success as predicted by humans. The results show that the metric based on physics simulation is a more consistent predictor for grasp success than the standard υ-metric. The results also support the hypothesis that human labels are not required for good ground truth grasp data. Instead the physics-metric can be used to generate datasets in simulation that may then be used to bootstrap learning in the real world. (ii) We apply a deep learning method and show that it can better leverage the large-scale database for prediction of grasp success compared to logistic regression. Furthermore, the results suggest that labels based on the physics-metric are less noisy than those from the υ-metric and therefore lead to a better classification performance.

关键词： Databases Robots Shape Three-dimensional displays Stability analysis Noise measurement

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The Coordinate Particle Filter - a novel Particle Filter for high dimensional systems

The Coordinate Particle Filter - a novel Particle Filter for...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Manuel Wüthrich Jeannette Bohg Daniel Kappler Claudia Pfreundt Stefan Schaal Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems Tübingen Germany High Performance Humanoid Technologies Lab Karlsruhe Institute of Technology Karlsruhe Germany Computational Learning and Motor Control lab at the University of Southern California Los Angeles CA USA

Parametric filters, such as the Extended Kalman Filter and the Unscented Kalman Filter, typically scale well with the dimensionality of the problem, but they are known to fail if the posterior state distribution canno... 详细信息

ISBN: (纸本)9781479969241

关键词： Noise Particle filters Approximation methods Standards Monte Carlo methods Proposals Trajectory

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Humanoid Momentum Estimation Using Sensed Contact Wrenches

Humanoid Momentum Estimation Using Sensed Contact Wrenches

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IEEE-RAS International Conference on Humanoid Robots

作者： Nicholas Rotella Alexander Herzog Stefan Schaal Ludovic Righetti Computational Learning and Motor Control Lab University of Southern California Autonomous Motion Department Max Planck Institute for Intelligent Systems

ISBN: (纸本)9781479968862

This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches.

关键词： spanners Momentum Estimators pendulum model Humanoid robots Robot Humanoid center of mass

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Efficient Bayesian Local Model learning for control

Efficient Bayesian Local Model Learning for Control

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Franziska Meier Philipp Hennig Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Angeles CA 90089 USA Max-Planck-Institute for Intelligent Systems 72076 Tubingen Germany

ISBN: (纸本)9781479969357

Model-based control is essential for compliant control and force control in many modern complex robots, like humanoid or disaster robots. Due to many unknown and hard to model nonlinearities, analytical models of such robots are often only very rough approximations. However, modern optimization controllers frequently depend on reasonably accurate models, and degrade greatly in robustness and performance if model errors are too large. For a long time, machine learning has been expected to provide automatic empirical model synthesis, yet so far, research has only generated feasibility studies but no learning algorithms that run reliably on complex robots. In this paper, we combine two promising worlds of regression techniques to generate a more powerful regression learning system. On the one hand, locally weighted regression techniques are computationally efficient, but hard to tune due to a variety of data dependent meta-parameters. On the other hand, Bayesian regression has rather automatic and robust methods to set learning parameters, but becomes quickly computationally infeasible for big and high-dimensional data sets. By reducing the complexity of Bayesian regression in the spirit of local model learning through variational approximations, we arrive at a novel algorithm that is computationally efficient and easy to initialize for robust learning. Evaluations on several datasets demonstrate very good learning performance and the potential for a general regression learning tool for robotics.

关键词： Robot compliance control Machine time Bayesian inference learning Humanoid Dataset model error learning algorithms Standing Orders Force control AUTOMATIC tuning robotics

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Full dynamics LQR control of a humanoid robot: An experimental study on balancing and squatting

Full dynamics LQR control of a humanoid robot: An experiment...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Sean Mason Ludovic Righetti Stefan Schaal Computational Learning and Motor Control Lab University of Southern California Los Anleles California Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

Humanoid robots operating in human environments require whole-body controllers that can offer precise tracking and well-defined disturbance rejection behavior. In this contribution, we propose an experimental evaluation of a linear quadratic regulator (LQR) using a linearization of the full robot dynamics together with the contact constraints. The advantage of the controller is that it explicitly takes into account the coupling between the different joints to create optimal feedback controllers for whole-body control. We also propose a method to explicitly regulate other tasks of interest, such as the regulation of the center of mass of the robot or its angular momentum. In order to evaluate the performance of linear optimal control designs in a real-world scenario (model uncertainty, sensor noise, imperfect state estimation, etc), we test the controllers in a variety of tracking and balancing experiments on a torque controlled humanoid (e.g. balancing, split plane balancing, squatting, pushes while squatting, and balancing on a wheeled platform). The proposed control framework shows a reliable push recovery behavior competitive with more sophisticated balance controllers, rejecting impulses up to 11.7 Ns with peak forces of 650 N, with the added advantage of great computational simplicity. Furthermore, the controller is able to track squatting trajectories up to 1 Hz without relinearization, suggesting that the linearized dynamics is sufficient for significant ranges of motion.

关键词： Joints Humanoid robots Iron Dynamics Aerospace electronics Couplings

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Robot arm pose estimation through pixel-wise part classification

Robot arm pose estimation through pixel-wise part classifica...

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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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