Passer Kinematic Cues for Object Weight Prediction in a Simulated Robot-Human Handover

Günter, Clara; Figueredo, Luis; Hermsdörfer, Joachim; Franklin, David W.

Abstract

Object handovers, a seemingly straightforward action, involve a complex interplay of predictive and reactive control mechanisms in both partners. Understanding the cues that are used by humans to predict object properties is needed for planning natural robot handovers. In human-human interactions, the receiver can extract information from the passer's movement. Here, we show in a VR simulated agenthuman object handover, that the human receiver can use passer kinematic cues to predict the transported object's properties, such as weight, and preemptively adapt the grasping strategy towards them. We show that when the agent's movement is correlated to the object weight, humans can interpret this cue and produce proportional anticipatory grip forces before object release. This adaptation is learned even when objects are presented in a random order and is strengthened with the repeated presentation of the pairing. The outcome of this study contributes to a better understanding of non-verbal cues in handover tasks and enables more transparent and efficient real-world physical robot-human interactions.

Citation

Günter, C., Figueredo, L., Hermsdörfer, J., & Franklin, D. W. (2024, November). Passer Kinematic Cues for Object Weight Prediction in a Simulated Robot-Human Handover. Presented at 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids), Nancy, France

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)
• Start Date: Nov 22, 2024
• End Date: Nov 24, 2024
• Online Publication Date: Dec 3, 2024
• Publication Date: Nov 22, 2024
• Deposit Date: Mar 21, 2025
• Publicly Available Date: Mar 25, 2025
• Print ISSN: 2164-0572
• Electronic ISSN: 2164-0580
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 173-180
• DOI: https://doi.org/10.1109/Humanoids58906.2024.10769881
• Public URL: https://nottingham-repository.worktribe.com/output/45862671
• Publisher URL: https://ieeexplore.ieee.org/document/10769881