Flexible Robotic Grasping with Sim-to-Real Transfer based Reinforcement Learning
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Robotic manipulation requires a highly flexible and compliant system. Task-specific heuristics are usually not able to cope with the diversity of the world outside of specific assembly lines and cannot generalize well. Reinforcement learning methods provide a way to cope with uncertainty and allow robots to explore their action space to solve specific tasks. However, this comes at a cost of high training times, sparse and therefore hard to sample useful actions, strong local minima, etc. In this paper we show a real robotic system, trained in simulation on a pick and lift task, that is able to cope with different objects. We introduce an adaptive learning mechanism that allows the algorithm to find feasible solutions even for tasks that would otherwise be intractable. Furthermore, in order to improve the performance on difficult objects, we use a prioritized sampling scheme. We validate the efficacy of our approach with a real robot in a pick and lift task of different objects.
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