Benchmarking Learning Algorithms for Dexterous Multi-Arm Insertion of Semi-Deformable Objects

Most of our everyday activities, such as the usage of tools to grasp small objects, require coordinated motion of the arms (e.g., reaching) or the hands (e.g., manipulation). Performing these tasks with one arm is often infeasible, mainly because the dexterity and flexibility required for such tasks is beyond a single arm’s capabilities. Similarly, a single robot arm is simply incapable of meeting the requirements of such complex tasks. A dual or multi-arm robotic system, on the other hand, extends the flexibility and capabilities of a single robot arm. It allows, for example, highly complex manipulation of small and deformable objects that would otherwise be infeasible for single arm systems. Yet, we are far from having robotic systems that can robustly achieve such dexterous manipulation skills. One, however, can envision a very broad range of applications in households and factories that can benefit from such strategies. Examples include, placing and closing lids, packaging boxes in pallets, inserting USB cable into sockets. Completing this type of tasks requires to localize both objects with respect to one another (lid on top of box, cable into tube) and to adapt forces and movements of the two arms in coordination. Insertion of semideformable materials is made particularly difficult as one cannot build an explicit model of the deformation and interaction forces. Machine learning provides a structured framework that can allow robots to learn difficult-to-model problems by using their previous experience, without explicit modeling of the task constraints and possibly taking advantage of noisy expert demonstrations. This benchmark proposes an evaluation of machine learning algorithms on a difficult multi-arm insertion task that involves collaboration among the arms to manipulate a small and semideformable object.