Robot Learning with Crash Constraints

Abstract

In the past decade, numerous machine learning algorithms have been shown to successfully learn optimal policies to control real robotic systems. However, it is common to encounter failing behaviors as the learning loop progresses. Specifically, in robot applications where failing is undesired but not catastrophic, many algorithms struggle with leveraging data obtained from failures. This is usually caused by (i) the failed experiment ending prematurely, or (ii) the acquired data being scarce or corrupted. Both complicate the design of proper reward functions to penalize failures. In this paper, we propose a framework that addresses those issues. We consider failing behaviors as those that violate a constraint and address the problem of learning with crash constraints, where no data is obtained upon constraint violation. The no-data case is addressed by a novel GP model (GPCR) for the constraint that combines discrete events (failure/success) with continuous observations (only obtained upon success). We demonstrate the effectiveness of our framework on simulated benchmarks and on a real jumping quadruped, where the constraint threshold is unknown a priori. Experimental data is collected, by means of constrained Bayesian optimization, directly on the real robot. Our results outperform manual tuning and GPCR proves useful on estimating the constraint threshold.

arXiv comment8 pages, 4 figures, 1 table, 1 algorithm. Accepted for publication in IEEE Robotics and Automation Letters (RA-L). Video demonstration of the experiments available at https://youtu.be/RAiIo0l6_rE . Algorithm implementation available at https://github.com/alonrot/classified_regression.git

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• A Literature Survey of Benchmark Functions For Global Optimization Problems
• Safe Controller Optimization for Quadrotors with Gaussian Processes
• An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
• Automatic LQR Tuning Based on Gaussian Process Global Optimization
• Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
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Cited by in corpus (3)

• GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
• Safe Value Functions
• Local Bayesian Optimization for Controller Tuning with Crash Constraints