Safe, Fluent and Acceptable Motion Generation and Execution for Human--Robot Interaction in Manufacturing Environments

2026-06-07

AI Summary

This study addresses the limitations of existing robot motion planning approaches in human-robot collaborative manufacturing environments, which often neglect psychological and social factors, thereby failing to simultaneously ensure physical safety and human-perceived behavioral acceptability. To bridge this gap, the authors propose a model predictive control (MPC) framework that integrates safety with interaction quality, systematically identifying key motion parameters influencing human perception. For the first time in manufacturing contexts, the framework incorporates social awareness and psychological cognition into motion generation, enabling tunable robot behaviors across four distinct social dimensions. User studies demonstrate that the generated behaviors significantly influence non-expert participants’ assessments of social acceptability, underscoring the critical role of human-centered design in shared workspaces.

Abstract

Robots operating in human environments must not only ensure physical safety but also exhibit behaviors that are understandable, fluent, and acceptable to human partners. This paper investigates motion generation strategies that combine safety guarantees with interaction quality considerations, such as motion smoothness and human comfort. While the design of robots capable of ensuring safety in shared human-robot environments has enabled closer and more advanced forms of interaction, these new proximity-based tasks require moving beyond purely technical considerations. In particular, robot behavior must also be addressed from psycho-cognitive and social perspectives. In this context, we argue for the relevance of integrating social-aware motion control into robotic systems. First, we identify the motion parameters that influence human perception and operator experience. Then, we implement a Model Predictive Control (MPC) framework that generates four distinct socially-informed robot behaviors. Finally, we conduct a user study to evaluate and validate these behaviors and assess their social impact on non-expert participants. The results demonstrate that variations in robot behavior significantly affect the perceived social acceptability of the system. These findings highlight the importance of incorporating human-centered considerations into motion generation strategies for robots operating in shared environments.

Problem

• human-robot interaction
• social acceptability
• motion generation
• human-centered robotics
• safety

Innovation

• social-aware motion control
• Model Predictive Control (MPC)
• human-robot interaction
• motion fluency
• social acceptability