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Unsupervised Contact learning for Humanoid Estimation and control

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Unsupervised Contact Learning for Humanoid Estimation and Co...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Nicholas Rotella Stefan Schaal Ludovic Righetti Computational Learning and Motor Control Lab University of Southern California Los Angeles California New York University New York New York

This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.

关键词： Friction Sensors Force Foot State estimation computational modeling

An MPC Walking Framework With External Contact Forces

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An MPC Walking Framework With External Contact Forces

IEEE International Conference on Robotics and Automation

作者： Sean Mason Nicholas Rotella Stefan Schaal Ludovic Righetti Computational Learning and Motor Control Lab University of Southern California Los Angeles California Tandon School of Engineering New York University New York USA

In this work, we present an extension to a linear Model Predictive control (MPC) scheme that plans external contact forces for the robot when given multiple contact locations and their corresponding friction cone. To this end, we set up a two-step optimization problem. In the first optimization, we compute the Center of Mass (CoM) trajectory, foot step locations, and introduce slack variables to account for violating the imposed constraints on the Zero Moment Point (ZMP). We then use the slack variables to trigger the second optimization, in which we calculate the optimal external force that compensates for the ZMP tracking error. This optimization considers multiple contacts positions within the environment by formulating the problem as a Mixed Integer Quadratic Program (MIQP) that can be solved at a speed between 100-300 Hz. Once contact is created, the MIQP reduces to a single Quadratic Program (QP) that can be solved in real-time (< 1kHz). Simulations show that the presented walking control scheme can withstand disturbances 2-3× larger with the additional force provided by a hand contact.

关键词： Optimization Force Legged locomotion Trajectory Friction Dynamics

Probabilistic Recurrent State-Space Models

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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

Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization

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Virtual vs. real: Trading off simulations and physical exper...

2017 IEEE International Conference on Robotics and Automation, ICRA 2017

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 Tubingen Germany Zurich Switzerland

ISBN: (纸本)9781509046331

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. © 2017 IEEE.

关键词： Reinforcement learning

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

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

2017 IEEE International Conference on Robotics and Automation, ICRA 2017

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

ISBN: (纸本)9781509046331

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. © 2017 IEEE.

关键词： Inverted pendulum

learning task-specific dynamics to improve whole-body control

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arXiv 2018年

作者： Gams, Andrej Mason, Sean A. Ude, Aleš Schaal, Stefan Righetti, Ludovic Humanoid and Cognitive Robotics Lab Dept. of Automatics Bio-cybernetics and Robotics Jožef Stefan Institute Ljubljana Slovenia Computational Learning and Motor Control Lab University of Southern California Los AngelesCA United States Tandon School of Engineering New York University New York United States Max Planck Institute for Intelligent Systems Tuebingen Germany

In task-based inverse dynamics control, reference accelerations used to follow a desired plan can be broken down into feedforward and feedback trajectories. The feedback term accounts for tracking errors that are caused from inaccurate dynamic models or external disturbances. On underactuated, free-floating robots, such as humanoids, good tracking accuracy often necessitates high feedback gains, which leads to undesirable stiff behaviors. The magnitude of these gains is anyways often strongly limited by the control bandwidth. In this paper, we show how to reduce the required contribution of the feedback controller by incorporating learned task-space reference accelerations. Thus, we i) improve the execution of the given specific task, and ii) offer the means to reduce feedback gains, providing for greater compliance of the system. In contrast to learning task-specific joint-torques, which might produce a similar effect but can lead to poor generalization, our approach directly learns the task-space dynamics of the center of mass of a humanoid robot. Simulated and real-world results on the lower part of the Sarcos Hermes humanoid robot demonstrate the applicability of the approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Feedback

On the Design of LQR Kernels for Efficient controller learning

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On the Design of LQR Kernels for Efficient Controller Learni...

IEEE Annual Conference on Decision and control

作者： Alonso Marco Philipp Hennig Stefan Schaal Sebastian Trimpe Max Planck Institute for Intelligent Systems Tübingen Germany Computational Learning and Motor Control Lab University of Southern California Los Angeles USA

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.

关键词： Kernel Bayes methods Optimization Standards Adaptive control Nonlinear dynamical systems Optimal control

On the design of LQR kernels for efficient controller learning

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arXiv 2017年

作者： Marco, Alonso Hennig, Philipp Schaal, Stefan Trimpe, Sebastian Max Planck Institute for Intelligent Systems Tübingen72076 Germany Computational Learning and Motor Control Lab University of Southern California Los Angeles United States

关键词： controllers

Balancing and walking using full dynamics LQR control with contact constraints

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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

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

关键词： controllers