Rigid Object

Rigid object manipulation is a core area of robotics research focused on understanding and controlling the interaction of rigid bodies in various environments. Current research emphasizes developing robust and scalable simulation methods, often employing graph neural networks and signed distance functions to represent object shapes and interactions, as well as improving pose estimation techniques using deep learning and keypoint-based approaches. These advancements are crucial for improving robotic dexterity in tasks ranging from assembly and manipulation to object tracking and scene understanding, impacting fields like manufacturing, surgery, and augmented reality.