Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation

2026-05-03

AI Summary

This work addresses the inefficiency of conventional deep reinforcement learning navigation methods, which reset the entire environment after a single collision, severely hindering exploration in complex obstacle-rich scenarios. To overcome this limitation, the authors propose the Multi-Collision Budget (MCB) framework, which decouples local collision termination from global environment resets, enabling agents to attempt challenging configurations multiple times within a single episode. By introducing an adjustable collision budget, MCB abandons the rigid “one collision equals failure” training paradigm while preserving deployment safety. Experimental results demonstrate that MCB substantially improves task success rates and navigation efficiency on both simulated and real robotic platforms, with the most pronounced gains observed under low collision budgets, and significantly accelerates early-stage training convergence.

Abstract

Should a single collision necessarily terminate an entire navigation episode? In most deep reinforcement learning (DRL) frameworks for robot navigation, this remains the standard practice: every collision immediately triggers a global environment reset and is penalized as a complete task failure. While a collision during deployment naturally indicates task failure, applying the same treatment during training prevents the agent from exploring challenging obstacle configurations, which slows learning progress in the early training phase. In this work, we challenge this convention and propose a Multi-Collision reset Budget (MCB) framework that decouples local collision termination from global environment resets, allowing the agent to retry difficult configurations within the same episode. Experiments on multiple simulated and real-world robotic platforms show that the framework accelerates early-stage exploration and improves both success rate and navigation efficiency over conventional single-collision reset baselines, with a small collision budget producing the largest gains.

Problem

• robot navigation
• collision handling
• deep reinforcement learning
• environment reset
• exploration efficiency

Innovation

• Multi-Collision reset Budget
• collision handling
• robot navigation
• deep reinforcement learning
• environment reset