Robust Robot Navigation through Failure-Aversion Learning

Abstract

Autonomous navigation in complex dynamic environments remains a fundamental challenge in robotics, and many reinforcement learning (RL) algorithms have demonstrated promising results, especially the on-policy ones. However, the inherent sample efficiency issue is still a fundamental problem to be solved. Methods integrating off-policy approaches into on-policy frameworks have been proposed to improve the sample efficiency by focusing on imitating the agent’s past exemplary experiences while discarding less optimal ones. However, these methods overlook the valuable insights embedded within failures. Although some research has begun to explore learning from failures, it is usually done at a point-by-point level, ignoring the rich sequence context inherent in the trajectory. In this paper, we introduce DFPS-Nav, a training framework that utilizes Failure-Aversion Learning (FAL) to perform segmented, trend-based credit assignment, identifying both failure-inducing actions and valuable recovery behaviors within failed trajectories. We further improve successful imitation by adopting Prioritized Self-Imitation Learning (PSIL), which scores trajectories and prioritizes high-quality behaviors so that successful behaviors are reliably reproduced. Extensive simulation and real-world experiments demonstrate that using both FAL and PSIL to extract and refine information from the sequential context within trajectories, DFPS-Nav achieves up to 29.5% and 27% higher success rates in static and dynamic environments compared to the strong baseline method and successfully is applied in the real world. This work underscores how systematically deconstructing failures while prioritizing successes leads to more efficient and robust autonomous navigation.