Learning Robot Safety from Sparse Human Feedback using Conformal Prediction

TL;DR

This paper introduces a sample-efficient method using conformal prediction to identify and warn about unsafe states in robot policies based on sparse human feedback, enhancing safety and enabling policy improvements.

Contribution

It presents a novel approach combining conformal prediction with human feedback to reliably detect unsafe states in robot policies, improving safety guarantees.

Findings

Successfully detects unsafe quadcopter states in real-time

Improves safety of model predictive control in navigation tasks

Achieves guaranteed safety miss rate with minimal data

Abstract

Ensuring robot safety can be challenging; user-defined constraints can miss edge cases, policies can become unsafe even when trained from safe data, and safety can be subjective. Thus, we learn about robot safety by showing policy trajectories to a human who flags unsafe behavior. From this binary feedback, we use the statistical method of conformal prediction to identify a region of states, potentially in learned latent space, guaranteed to contain a user-specified fraction of future policy errors. Our method is sample-efficient, as it builds on nearest neighbor classification and avoids withholding data as is common with conformal prediction. By alerting if the robot reaches the suspected unsafe region, we obtain a warning system that mimics the human's safety preferences with guaranteed miss rate. From video labeling, our system can detect when a quadcopter visuomotor policy will fail to steer through a designated gate. We present an approach for policy improvement by avoiding the suspected unsafe region. With it we improve a model predictive controller's safety, as shown in experimental testing with 30 quadcopter flights across 6 navigation tasks. Code and videos are provided.