Robot Handover

Robot handover, the process of transferring objects between robots and humans, aims to create seamless and safe interactions in collaborative settings. Current research emphasizes improving handover success rates through techniques like incorporating semantic and geometric information from vision and language models, developing compliant control strategies for blind handovers, and using machine learning (including deep reinforcement learning, Gaussian processes, and LSTM networks) to predict handover events and optimize trajectories. These advancements are crucial for integrating robots into human environments, enhancing human-robot collaboration in various applications, from industrial settings to assistive robotics and service industries.