Biomimetic Cues Enable Predictive Mechanisms in Simulated and Physical Robot-Human Object Handovers

Abstract Object handovers – while representing one of the simplest forms of physical interaction between two agents – involve a complex interplay of predictive and reactive control mechanisms in both agents. As human-human pairs have unrivaled skills in physical collaboration tasks, we take the approach of understanding and applying biomimetic concepts to human-robot interaction. Here, we apply the concept of passer movement cues, that is, slower movement for heavy objects and faster movements for lighter objects, to robot-human handovers. We first show that when a simulated passing agent’s movement is scaled with object mass, participants as receivers adapt their anticipatory grip forces according to mass in a virtual environment. We then apply the same concept to a physical robot-human handover and show that our approach generalizes to the real-world. The predictive scaling of grip forces is learned iteratively upon repeated presentations of trajectory-mass pairings, whether the masses are presented in a random or blocked order. Overall we demonstrate that the presentation of robotic kinematic cues can provide intuitive and naturalistic human predictive control in object handover. This extends the use of non-verbal cues in robot-human handover tasks and facilitates more legible and efficient physical robot-human interactions. Competing Interest Statement The authors have declared no competing interest. Footnotes Authors’ Contact Information: Clara Günter, Technical University of Munich, Neuromuscular Diagnostics, School of Medicine and Health, Munich Institute of Robotics and Machine Intelligence (MIRMI), Munich, Germany, clara.guenter{at}tum.de; Yuhe Gong, University of Nottingham, Cyber-physical Health and Assistive Robotics Technologies Research Group (CHART), School of Computer Science, Nottingham, UK, yuhe.gong{at}nottingham.ac.uk; Riddhiman Laha, Technical University of Munich, School of Computation, Information, and Technology (CIT), MIRMI, Munich, Germany, riddhiman.laha{at}tum.de; Simon Appoltshauser, Technical University of Munich, Neuromuscular Diagnostics, School of Medicine and Health, MIRMI, Munich, Germany, simon.appoltshauser{at}tum.de; Luis Figueredo, University of Nottingham, CHART, School of Computer Science, Nottingham, UK and Technical University of Munich, MIRMI, Munich, Germany, luis.figueredo{at}nottingham.ac.uk; Joachim Hermsdörfer, Technical University of Munich, Human Movement Science, School of Medicine and Health, MIRMI, Munich, Germany, joachim.hermsdoerfer{at}tum.de; David W. Franklin, Technical University of Munich, Neuromuscular Diagnostics, School of Medicine and Health, MIRMI, Munich Data Science Institute (MDSI), Munich, Germany, david.franklin{at}tum.de. CCS Concepts • Human-centered computing → User studies. ACM Reference Format Clara Günter, Yuhe Gong, Riddhiman Laha, Simon Appoltshauser, Luis Figueredo, Joachim Hermsdörfer, and David W. Franklin. 2025. Biomimetic Cues Enable Predictive Mechanisms in Simulated and Physical Robot-Human Object Handovers. ACM Trans. Hum.-Robot Interact. x, 1 (October 2025), 23 pages. https://doi.org/x