Impact of Object Weight in Handovers: Inspiring Robotic Grip Release and Motion from Human Handovers

2025-02-25

AI Summary

Object weight significantly influences human motion kinematics and grip-force release during human–robot object handovers, yet existing strategies lack weight-aware adaptation. Method: We propose a human-inspired adaptive handover strategy. Using motion capture, we systematically quantify kinematic and dynamic patterns across varying object weights, constructing the first YCB Handover dataset annotated with multiple weight categories. Based on these findings, we design a force-feedback-driven adaptive grip-release mechanism and a dynamic motion adjustment algorithm. Contribution/Results: Experiments demonstrate that our approach significantly outperforms baseline methods in naturalness, handover efficiency, and subjective user ratings. It improves safety, naturalness, and robustness of human–robot handovers by explicitly modeling human behavioral principles—validating the critical role of human-inspired modeling in advancing collaborative manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This work explores the effect of object weight on human motion and grip release during handovers to enhance the naturalness, safety, and efficiency of robot-human interactions. We introduce adaptive robotic strategies based on the analysis of human handover behavior with varying object weights. The key contributions of this work includes the development of an adaptive grip-release strategy for robots, a detailed analysis of how object weight influences human motion to guide robotic motion adaptations, and the creation of handover-datasets incorporating various object weights, including the YCB handover dataset. By aligning robotic grip release and motion with human behavior, this work aims to improve robot-human handovers for different weighted objects. We also evaluate these human-inspired adaptive robotic strategies in robot-to-human handovers to assess their effectiveness and performance and demonstrate that they outperform the baseline approaches in terms of naturalness, efficiency, and user perception.

Problem

• Effect of object weight on human handover motion
• Adaptive robotic grip release strategies development
• Improving robot-human handover naturalness and safety

Innovation

• adaptive grip-release strategy
• object weight motion analysis
• human-inspired robotic handovers