Robot Learning with Crash Constraints
This addresses a specific issue in robotics for applications where failures are non-catastrophic but data-scarce, offering an incremental improvement over existing methods.
The paper tackles the problem of robot learning where failures yield no data, proposing a framework with a novel Gaussian Process model (GPCR) that combines discrete failure events with continuous success observations to handle crash constraints. Results show effectiveness on simulated benchmarks and a real jumping quadruped, outperforming manual tuning and estimating unknown constraint thresholds.
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.