Object Handover

Object handover, the seamless transfer of objects between robots and humans, is a crucial area of robotics research aiming to improve human-robot collaboration. Current research focuses on developing robust algorithms, often employing deep neural networks processing visual and force/torque sensor data, to predict optimal grasps, detect handover failures (including human-induced ones), and coordinate the timing and trajectory of the handover process. These advancements utilize techniques like task-space quadratic programming and multimodal data fusion to achieve reliable and natural interactions, impacting fields like manufacturing and service robotics.