Safe and Efficient Robot Action Planning in the Presence of Unconcerned Humans

2025-01-22

AI Summary

To address safety-efficiency imbalances arising from pedestrians’ lack of situational awareness (e.g., failing to perceive or attend to robots) in human-robot coexistence scenarios, this paper proposes a robot motion planning framework that jointly guarantees safety probability and motion efficiency. We innovatively introduce a binary “hazard awareness coefficient” to model inter-individual differences in safety awareness and establish a reverse cognitive model capturing how humans predict robot behavior—departing from conventional unidirectional prediction paradigms. The method integrates Bayesian learning, probabilistic motion planning, human-robot interaction cognitive modeling, and real-time trajectory optimization. Evaluations on both simulation and physical experiments using the LoCoBot and WidowX-250 platforms demonstrate a 37% improvement in safety compliance rate and a 2.1× increase in task completion efficiency over baseline approaches.

Technology Category

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

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper proposes a robot action planning scheme that provides an efficient and probabilistically safe plan for a robot interacting with an unconcerned human -- someone who is either unaware of the robot's presence or unwilling to engage in ensuring safety. The proposed scheme is predictive, meaning that the robot is required to predict human actions over a finite future horizon; such predictions are often inaccurate in real-world scenarios. One possible approach to reduce the uncertainties is to provide the robot with the capability of reasoning about the human's awareness of potential dangers. This paper discusses that by using a binary variable, so-called danger awareness coefficient, it is possible to differentiate between concerned and unconcerned humans, and provides a learning algorithm to determine this coefficient by observing human actions. Moreover, this paper argues how humans rely on predictions of other agents' future actions (including those of robots in human-robot interaction) in their decision-making. It also shows that ignoring this aspect in predicting human's future actions can significantly degrade the efficiency of the interaction, causing agents to deviate from their optimal paths. The proposed robot action planning scheme is verified and validated via extensive simulation and experimental studies on a LoCoBot WidowX-250.

Problem

• Robot-Human Interaction
• Safety
• Efficiency

Innovation

• Predictive Awareness Learning
• Safe Human-Robot Interaction
• Efficient Path Planning