Meta-Active Learning in Probabilistically Safe Optimization

Abstract

When a robotic system is faced with uncertainty, the system must take calculated risks to gain information as efficiently as possible while ensuring system safety. The need to safely and efficiently gain information in the face of uncertainty spans domains from healthcare to search and rescue. To efficiently learn when data is scarce or difficult to label, active learning acquisition functions intelligently select a data point that, if the label were known, would most improve the estimate of the unknown model. Unfortunately, prior work in active learning suffers from an inability to accurately quantify information-gain, generalize to new domains, and ensure safe operation. To overcome these limitations, we develop Safe MetAL, a probabilistically-safe, active learning algorithm which meta-learns an acquisition function for selecting sample efficient data points in safety critical domains. The key to our approach is a novel integration of meta-active learning and chance-constrained optimization. We (1) meta-learn an acquisition function based on sample history, (2) encode this acquisition function in a chance-constrained optimization framework, and (3) solve for an information-rich set of data points while enforcing probabilistic safety guarantees. We present state-of-the-art results in active learning of the model of a damaged UAV and in learning the optimal parameters for deep brain stimulation. Our approach achieves a 41% improvement in learning the optimal model and a 20% speedup in computation time compared to active and meta-learning approaches while ensuring safety of the system.