Hand Object Reorientation

Hand object reorientation focuses on enabling robots to dexterously manipulate objects within their grasp, achieving rotations and orientations similar to human hand movements. Current research emphasizes developing robust and efficient control algorithms, often employing reinforcement learning and novel contact modeling techniques like signed distance functions (SDFs), to achieve reliable reorientation across diverse object shapes and hand orientations, even under challenging conditions like downward-facing hands. This research is crucial for advancing robotic dexterity, impacting fields like manufacturing, surgery, and assistive robotics by enabling robots to perform complex tasks requiring in-hand manipulation.