Maximum Likelihood Constraint Inference from Stochastic Demonstrations

TL;DR

This paper extends maximum likelihood constraint inference to stochastic systems using maximum causal entropy likelihoods, enabling risk-aware constraint identification with efficient algorithms.

Contribution

It introduces a novel approach for stochastic constraint inference using maximum causal entropy, unifying likelihood and risk tolerance estimation within Bellman backups.

Findings

Effective inference of constraints in stochastic systems

Unified algorithm for likelihood and risk tolerance computation

No additional computational complexity introduced

Abstract

When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demonstrations can be computed, given the system dynamics and task objective, and the maximum likelihood constraints can be identified. Prior constraint inference work has focused mainly on deterministic models. Stochastic models, however, can capture the uncertainty and risk tolerance that are often present in real systems of interest. This paper extends maximum likelihood constraint inference to stochastic applications by using maximum causal entropy likelihoods. Furthermore, we propose an efficient algorithm that computes constraint likelihood and risk tolerance in a unified Bellman backup, allowing us to generalize to stochastic systems without increasing computational complexity.