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No abstract

In this work, we present a whole-arm grasping strategy for soft arms whose task is to capture space debris. The non-cooperative nature of space debris and the characteristics of the space environment enforce high-level requirements for robotic arms, especially dexterity. Taking inspiration from the outstanding capabilities of the elephant trunk in grasping, we formulated a grasping strategy based upon the identification of contact points on the object to force the bending of the arm and induce the wrapping around the object, as the animal model does. This strategy is implemented by leveraging on coupled Finite Element simulations of a trunk-like soft arm and Reinforcement Learning tools to learn the grasping. The results show that the robot successfully learns the task by moving the proximal part closer to the object and using the distal one to wrap around the object. We show that the obtained policy is valid for diverse object sizes and positions. Our grasping strategy is the first example of bio-inspired whole-arm grasping for a soft arm in space. We believe that, in the near future, this strategy will enable new grasping capabilities in soft arms.