Extrinsic Dexterity

Extrinsic dexterity in robotics focuses on enabling robots to manipulate objects by leveraging the surrounding environment, rather than solely relying on the robot's own manipulators. Current research emphasizes developing robust control policies, often employing diffusion models, reinforcement learning, and graph neural networks, to achieve dexterous manipulation tasks such as precise insertion, reorientation of ungraspable objects, and pre-grasp manipulation. This research is significant because it expands the capabilities of robots to handle a wider range of manipulation tasks in unstructured environments, with applications in manufacturing, service robotics, and assistive technologies. The integration of tactile sensing and vision-based feedback is a key trend, improving the accuracy and adaptability of these methods.