Adapting Skills to Novel Grasps: A Self-Supervised Approach
This addresses inefficiencies in robot manipulation for everyday tasks, offering a self-supervised solution that is incremental but practical.
The paper tackles the problem of adapting manipulation trajectories for grasped objects to novel grasp poses without requiring explicit trajectory definitions for each grasp, and achieves a 28.5% higher success rate compared to the best baseline in real-world experiments.
In this paper, we study the problem of adapting manipulation trajectories involving grasped objects (e.g. tools) defined for a single grasp pose to novel grasp poses. A common approach to address this is to define a new trajectory for each possible grasp explicitly, but this is highly inefficient. Instead, we propose a method to adapt such trajectories directly while only requiring a period of self-supervised data collection, during which a camera observes the robot's end-effector moving with the object rigidly grasped. Importantly, our method requires no prior knowledge of the grasped object (such as a 3D CAD model), it can work with RGB images, depth images, or both, and it requires no camera calibration. Through a series of real-world experiments involving 1360 evaluations, we find that self-supervised RGB data consistently outperforms alternatives that rely on depth images including several state-of-the-art pose estimation methods. Compared to the best-performing baseline, our method results in an average of 28.5% higher success rate when adapting manipulation trajectories to novel grasps on several everyday tasks. Videos of the experiments are available on our webpage at https://www.robot-learning.uk/adapting-skills