2020/10/31 by Alonso Marco, Dominik Baumann, Majid Khadiv +3
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Bayesian probability #Constraint (computer-aided design) #Constraint learning #Control (management) #Crash #Machine Learning and Algorithms #Reinforcement Learning in Robotics #Robot #cs.LG #cs.RO
paper · pdf · doi:10.1109/lra.2021.3057055
published as IEEE Robotics and Automation Letters, Vol 6(2), pp. 1439-1446, 2021 · 8 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
openalex created_date 2020/10/22 · arxiv created 2021/01/28 · openalex publication_date 2021/02/03 · arxiv updated 2021/02/26 · openalex updated_date 2026/08/05
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 letter, 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.