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Using simulation to train robot manipulation policies holds the promise of analmost unlimited amount of training data, generated safely out of harm's way.One of the key challenges of using simulation, to date, has been to bridge thereality gap, so that policies trained in simulation can be deployed in the realworld. We explore the reality gap in the context of learning a contextualpolicy for multi-fingered robotic grasping. We propose a Grasping ObjectsApproach for Tactile (GOAT) robotic hands, learning to overcome the reality gapproblem. In our approach we use human hand motion demonstration to initializeand reduce the search space for learning. We contextualize our policy with thebounding cuboid dimensions of the object of interest, which allows the policyto work on a more flexible representation than directly using an image or pointcloud. Leveraging fingertip touch sensors in the hand allows the policy toovercome the reduction in geometric information introduced by the coarsebounding box, as well as pose estimation uncertainty. We show our learnedpolicy successfully runs on a real robot without any fine tuning, thus bridgingthe reality gap.

Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies