Can a mobile robot learn from a pedestrian model to prevent the sidewalk salsa?

2025-08-29

AI Summary

Mobile robots navigating sidewalks often exhibit socially awkward avoidance behaviors—such as repetitive lateral oscillations (“sidewalk salsa”)—due to conventional navigation policies that ignore pedestrians’ implicit communication cues, resulting in inefficient and subjectively risky interactions. Method: We propose a novel risk-aware reinforcement learning (RL) framework integrated with a Communication-Enabled Interaction (CEI) pedestrian behavior model. For the first time, CEI is embedded into an RL architecture to enable robots to explicitly convey motion intent via interpretable, communicative actions—facilitating implicit human–robot coordination. Contribution/Results: (1) We design a risk-aware reward function that explicitly models pedestrians’ perceived risk; (2) the trained agent significantly reduces both the intensity of pedestrian evasive maneuvers and subjective risk ratings. Experiments demonstrate improved naturalness, safety, and social acceptability of robot navigation in dense pedestrian environments.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Pedestrians approaching each other on a sidewalk sometimes end up in an awkward interaction known as the "sidewalk salsa": they both (repeatedly) deviate to the same side to avoid a collision. This provides an interesting use case to study interactions between pedestrians and mobile robots because, in the vast majority of cases, this phenomenon is avoided through a negotiation based on implicit communication. Understanding how it goes wrong and how pedestrians end up in the sidewalk salsa will therefore provide insight into the implicit communication. This understanding can be used to design safe and acceptable robotic behaviour. In a previous attempt to gain this understanding, a model of pedestrian behaviour based on the Communication-Enabled Interaction (CEI) framework was developed that can replicate the sidewalk salsa. However, it is unclear how to leverage this model in robotic planning and decision-making since it violates the assumptions of game theory, a much-used framework in planning and decision-making. Here, we present a proof-of-concept for an approach where a Reinforcement Learning (RL) agent leverages the model to learn how to interact with pedestrians. The results show that a basic RL agent successfully learned to interact with the CEI model. Furthermore, a risk-averse RL agent that had access to the perceived risk of the CEI model learned how to effectively communicate its intention through its motion and thereby substantially lowered the perceived risk, and displayed effort by the modelled pedestrian. These results show this is a promising approach and encourage further exploration.

Problem

• Modeling pedestrian-robot interactions to prevent sidewalk salsa
• Leveraging pedestrian behavior model in robotic planning
• Using reinforcement learning for safe robot-pedestrian communication

Innovation

• Reinforcement Learning agent leveraging pedestrian model
• Risk-averse RL using perceived risk metrics
• Motion-based intention communication for collision avoidance