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This paper addresses the challenge of following a target human in crowded environments by decomposing the task into a sparse reward system and independent cost constraints within a multi-constraint reinforcement learning (RL) framework. By managing these constraints through explicit cost thresholds, the approach allows for a tunable balance between proximity to the target and safety from collisions, particularly in unpredictable pedestrian scenarios. Experimental results show that this method significantly improves the proximity-safety balance compared to traditional RL methods, with successful real-robot deployment further validating its practical application.
Explicitly managing safety and proximity in human-following tasks leads to a superior balance that outperforms conventional RL approaches, even in unpredictable environments.
Following a target human in crowded environments involves an inherent conflict between staying close to the target and navigating safely among surrounding pedestrians and obstacles. This conflict becomes more severe in dense scenarios, where aggressive following risks collisions and conservative margins lead to target loss, especially when pedestrian behaviors are unfamiliar or unpredictable. Existing reinforcement learning (RL) methods typically encode these competing objectives into a single dense reward, but the resulting proximity-safety balance is implicit and difficult to adjust across conditions. To address this, we decompose the human-following task into a sparse task reward and independent cost constraints within a multi-constraint RL formulation, where each constraint is managed through cost thresholds with direct behavioral meaning rather than implicit reward weight ratios, allowing explicit and tunable control over the trade-off. We further quantify the prediction uncertainty of human motions and integrate these estimates into the RL costs to enhance safety under unpredictable conditions. Extensive experiments across both in-distribution and out-of-distribution settings demonstrate that our method achieves an effective proximity-safety balance compared to baselines. Real-robot deployment further validates the feasibility of our method in real-world scenarios. More details are available on our project page: https://nav-ps-balance.github.io/.
