Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers
This addresses the challenge of seamless robot-to-human handovers for large, deformable, or delicate objects, representing an incremental improvement in robotics.
The paper tackled the problem of generating human-like bimanual robot motions for object handovers by proposing a framework that uses a Hidden Semi-Markov Model to reactively adapt trajectories based on human motion, with results showing it is perceived as more human-like compared to a baseline Inverse Kinematics approach.
Bimanual handovers are crucial for transferring large, deformable or delicate objects. This paper proposes a framework for generating kinematically constrained human-like bimanual robot motions to ensure seamless and natural robot-to-human object handovers. We use a Hidden Semi-Markov Model (HSMM) to reactively generate suitable response trajectories for a robot based on the observed human partner's motion. The trajectories are adapted with task space constraints to ensure accurate handovers. Results from a pilot study show that our approach is perceived as more human--like compared to a baseline Inverse Kinematics approach.