Robot to Human Object Handover

Robot-to-human object handover research focuses on enabling robots to seamlessly transfer objects to humans, prioritizing safety, ergonomics, and natural interaction. Current efforts concentrate on developing algorithms that leverage diverse sensor modalities (vision, tactile, torque) and machine learning techniques, including reinforcement learning and deep neural networks, to optimize grasps, predict human actions, and generate human-like handover motions. This research is crucial for advancing human-robot collaboration in various fields, improving workplace safety (e.g., in construction) and enabling more intuitive interaction in assistive robotics and manufacturing. Simulation environments are also being developed to facilitate benchmarking and algorithm development.