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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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Inertial Sensor-Based Humanoid Joint State Estimation

Inertial Sensor-Based Humanoid Joint State Estimation

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IEEE International Conference on Robotics and Automation

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

ISBN: (纸本)9781467380270

This work presents methods for the determination of a humanoid robot's joint velocities and accelerations directly from link-mounted Inertial Measurement Units (IMUs) each containing a three-axis gyroscope and a three-axis accelerometer. No information about the global pose of the floating base or its links is required and precise knowledge of the link IMU poses is not necessary due to presented calibration routines. Additionally, a filter is introduced to fuse gyroscope angular velocities with joint position measurements and compensate the computed joint velocities for time-varying gyroscope biases. The resulting joint velocities are subject to less noise and delay than filtered velocities computed from numerical differentiation of joint potentiometer signals, leading to superior performance in joint feedback control as demonstrated in experiments performed on a SARCOS hydraulic humanoid.

关键词： demonstrated experiments performed SARCOS

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Balancing and walking using full dynamics LQR control with contact constraints

Balancing and walking using full dynamics LQR control with c...

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IEEE-RAS International Conference on Humanoid Robots

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

ISBN: (纸本)9781509047192

Torque control algorithms which consider robot dynamics and contact constraints are important for creating dynamic behaviors for humanoids. As computational power increases, algorithms tend to also increase in complexity. However, it is not clear how much complexity is really required to create controllers which exhibit good performance. In this paper, we study the capabilities of a simple approach based on contact consistent LQR controllers designed around key poses to control various tasks on a humanoid robot. We present extensive experimental results on a hydraulic, torque controlled humanoid performing balancing and stepping tasks. This feedback control approach captures the necessary synergies between the DoFs of the robot to guarantee good control performance. We show that for the considered tasks, it is only necessary to re-linearize the dynamics of the robot at different contact configurations and that increasing the number of LQR controllers along desired trajectories does not improve performance. Our result suggest that very simple controllers can yield good performance competitive with current state of the art, but more complex, optimization-based whole-body controllers. A video of the experiments can be found at https://***/5T08CNKV1hw.

关键词： Legged locomotion Dynamics Trajectory Robot kinematics Couplings Humanoid robots

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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...

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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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Probabilistic Object Tracking using a Range Camera

Probabilistic Object Tracking using a Range Camera

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Manuel Wuthrich Peter Pastor Mrinal Kalakrishnan Jeannette Bohg Stefan Schaal Autonomous Motion Department at the Max-Planck-Institute for Intelligent Systems Tubingen Germany Computational Learning and Motor Control lab at the University of Southern California Los Angeles CA USA

ISBN: (纸本)9781467363563

We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compute a posterior distribution over the current object pose. Depending on whether a robot or a human manipulates the object, we employ a process model with or without knowledge of control inputs. Observations are obtained from a range camera. As opposed to previous object tracking methods, we explicitly model self-occlusions and occlusions from the environment, e.g, the human or robotic hand. This leads to a strongly non-linear observation model and additional dependencies in the Bayesian network. We employ a Rao-Blackwellised particle filter to compute an estimate of the object pose at every time step. In a set of experiments, we demonstrate the ability of our method to accurately and robustly track the object pose in real-time while it is being manipulated by a human or a robot.

关键词： Probabilistic Object Tracking Range Camera 6-DoF

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Balancing and walking using full dynamics LQR control with contact constraints

arXiv

引用

arXiv 2017年

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

关键词： controllers

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learning and Adaptation of Inverse Dynamics Models: A Comparison

Learning and Adaptation of Inverse Dynamics Models: A Compar...

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IEEE-RAS International Conference on Humanoid Robots

作者： Kevin Hitzler Franziska Meier Stefan Schaal Tamim Asfour Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Max-Planck-Institute of Intelligent Systems Germany and Computational Learning and Motor Control Lab University of Southern California USA

ISBN: (数字)9781538676301

ISBN: (纸本)9781538676318

Performing tasks with high accuracy while interacting with the real world requires a robot to have an exact representation of its inverse dynamics that can be adapted to new situations. In the past, various methods for learning inverse dynamics models have been proposed that combine the well-known rigid body dynamics with model-based parameter estimation, or learn directly on measured data using regression. However, there are still open questions regarding the efficiency of model-based learning compared to data-driven approaches as well as their capabilities to adapt to changing dynamics. In this paper, we compare the state-of-the-art inertial parameter estimation to a purely data-driven and a model-based approach on simulated and real data, collected with the humanoid robot Apollo. We further compare the adaptation capabilities of two models in a pick and place scenario while a) learning the model incrementally and b) extending the initially learned model with an error model. Based on this, we show the gap between simulation and reality and verify the importance of modeling nonlinear effects using regression. Furthermore, we demonstrate that error models outperform incremental learning regarding adaptation of inverse dynamics models.

关键词： Adaptation models Robots Mathematical model Inverse problems Dynamics Data models Parameter estimation

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Leveraging big data for grasp planning

Leveraging big data for grasp planning

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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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Probabilistic Recurrent State-Space Models

arXiv

引用

arXiv 2018年

作者： Doerr, Andreas Daniel, Christian Schiegg, Martin Nguyen-Tuong, Duy Schaal, Stefan Toussaint, Marc Trimpe, Sebastian Bosch Center for Artificial Intelligence Renningen Germany Max Planck Institute for Intelligent Systems Stuttgart/Tübingen Germany Computational Learning and Motor Control Lab University of Southern California United States Machine Learning and Robotics Lab University of Stuttgart Germany

State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) proved extremely successful in modeling complex time series data. Fully probabilistic SSMs, however, are often found hard to train, even for smaller problems. To overcome this limitation, we propose a novel model formulation and a scalable training algorithm based on doubly stochastic variational inference and Gaussian processes. In contrast to existing work, the proposed variational approximation allows one to fully capture the latent state temporal correlations. These correlations are the key to robust training. The effectiveness of the proposed PR-SSM is evaluated on a set of realworld benchmark datasets in comparison to stateof-the-art probabilistic model learning methods. Scalability and robustness are demonstrated on a high dimensional problem. Copyright © 2018, The Authors. All rights reserved.

关键词： State space methods

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Parameter learning for improving binary descriptor matching

Parameter learning for improving binary descriptor matching

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Bharath Sankaran Srikumar Ramalingam Yuichi Taguchi Computational Learning and Motor Control Lab University of Southern California Los Angeles 90089 United States of America Mitsubishi Electric Research Labs Cambridge MA 02139 United States of America

ISBN: (纸本)9781509037636

Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and are comparable to SIFT.

关键词： Hamming distance Lighting Standards Training data Feature extraction Measurement Real-time systems

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