LEGS: Learning Efficient Grasp Sets for Exploratory Grasping
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Previous work defined Exploratory Grasping, where a robot iteratively grasps and drops an unknown complex polyhedral object to discover a set of robust grasps for each recognizably distinct stable pose of the object. Recent work used a multi-armed bandit model with a small set of candidate grasps per pose; however, for objects with few successful grasps, this set may not include the most robust grasp. We present Learned Efficient Grasp Sets (LEGS), an algorithm that can efficiently explore thousands of possible grasps by constructing small active sets of promising grasps and uses learned confidence bounds to determine when, with high confidence, it can stop exploring the object. Experiments suggest that LEGS can identify a high-quality grasp more efficiently than prior algorithms which do not learn active sets. In simulation experiments, we measure the optimality gap between the success probability of the best grasp identified by LEGS and baselines and that of the true most robust grasp. After 3000 steps of exploration, LEGS outperforms baseline algorithms on 10 of the 14 Dex-Net Adversarial objects and 25 of the 39 EGAD! objects. We then develop a self-supervised grasping system, where the robot explores grasps with minimal human intervention. Physical experiments across 3 objects suggest that LEGS converges to high-performing grasps significantly faster than baselines. See <https://sites.google.com/view/legs-exp-grasping> for supplemental material and videos.
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