2026-08-10 · IROS 2026

Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds

Shiting Gong, Jianpeng Yao, Jinfeng Wang, Marco Pavone, Jiachen Li

Reinforcement learning & control Safety & evaluation

Published at IROS 2026. 0 citations, as of the last refresh.

Abstract

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/.

arXiv comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026); Project Website: https://nav-ps-balance.github.io/

Ten-minute slide kit

Six slides is the whole talk: what was broken, what people tried, what these authors did, what the numbers say, where it falls over, and the sentence people should remember.

SLIDE 1

The problem

Robots are increasingly expected to follow specific individuals in applications such as healthcare, companionship, and assistance [\[1\]](#page-7-0), requiring them to navigate safely among pedestrians and obstacles while maintaining close proximity to the target. This task involves an inherent conflict between proximity and safety. Unlike point-goal navigation, where the robot can freely reroute around obstacles, human following involves a moving target, and any detour to avoid collisions risks increasing the…

SLIDE 2

What came before

Not recoverable from the parsed text. Read this section in the paper yourself.

SLIDE 3

The method

An overview of our method is illustrated in Fig. 1. At each timestep *t*, the state *S<sup>t</sup>* consists of the robot's physical state, local occupancy grid maps representing static obstacles, and the current positions, predicted trajectories, and quantified prediction uncertainties of nearby humans. The state is processed by a unified policy network, where CNNencoded occupancy features and human and robot tokens are combined into a sequence and processed by self-attention mechanisms [\[26\]](#page-7-25),…

SLIDE 4

What they measured

Traditional subgoalguided methods (SG-HA\*, SG-ORCA, SG-MPC) exhibit substantially lower SR and higher CR and TLR compared to RL-based methods, indicating limited ability to maintain following behavior in the presence of dense, dynamic pedestrians. Among RL-based methods, our approach significantly outperforms OGM-HEIGHT, achieving a 25.76% higher SR while reducing overall CR by 18.56%, human CR by 10.72%, and obstacle CR to the lowest 5.44% among learning-based methods. These results validate the effectiveness of…

SLIDE 5

Where it breaks

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SLIDE 6

One-line takeaway

This work decomposes 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.

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