A Virtual Mechanical Interaction Layer Enables Resilient Human-to-Robot Object Handovers

Object handover is a common form of interaction widely used in collaborative tasks. However, achieving it efficiently remains a challenge. We address the problem of ensuring resilient robotic actions that can adapt to complex changes in object pose during human-to-robot object handovers. We propose the use of Virtual Model Control to create an interaction layer that controls the robot and adapts to the dynamic changes in the handover process. Additionally, we propose using augmented reality to facilitate bidirectional communication between humans and robots during handovers. Our controller demonstrated high resilience in experimental tests against various sources of uncertainties with a nearly perfect success rate and an average mean approach time of 4 seconds in one-time rotation or translation tests of four different objects, as well as a success rate of 100 in tests with random object motion and robot starting pose. We also performed a user study with 16 participants to understand human preferences for different robot control profiles and augmented reality visuals in object handovers. Our results showed a general preference for the proposed approach with a 98 success rate and a mean approach time of 4.9 seconds and revealed insights for improving the interaction with the user.