Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison

TLDR

Modeling social interactions is essential for mobile robots in human‑populated environments; IRL captures motivational factors rather than actions, yet feature selection is often ad hoc and lacks systematic evaluation. The paper introduces a software framework to systematically study how feature sets and learning algorithms affect socially compliant robot navigation. The framework evaluates two IRL approaches and multiple feature sets through large‑scale crowd navigation simulations. The study benchmarks the approaches using a set of objective and subjective performance metrics.

Abstract

For mobile robots which operate in human populated environments, modeling social interactions is key to understand and reproduce people's behavior. A promising approach to this end is Inverse Reinforcement Learning (IRL) as it allows to model the factors that motivate people's actions instead of the actions themselves. A crucial design choice in IRL is the selection of features that encode the agent's context. In related work, features are typically chosen ad hoc without systematic evaluation of the alternatives and their actual impact on the robot's task. In this paper, we introduce a new software framework to systematically investigate the effect features and learning algorithms used in the literature. We also present results for the task of socially compliant robot navigation in crowds, evaluating two different IRL approaches and several feature sets in large-scale simulations. The results are benchmarked according to a proposed set of objective and subjective performance metrics.