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Creating robots that can act autonomously in dynamic unstructured environments requires dealing with novel objects. Thus, an offline learning phase is not sufficient for recognizing and manipulating such objects. Rather, an autonomous robot needs to acquire knowledge through its own interaction with its environment, without using heuristics encoding human insights about the domain. Interaction also...
In this video, we present approaches for learning throwing and catching skills. We first show how a hitting skill (i.e., table tennis) can be learned using a combination of imitation and reinforcement learning. This hitting skill is subsequently generalized to a catching skill. Secondly, we show how a robot can adapt a throwing skill to new targets. Finally, we demonstrate that a BioRob and a Barrett...
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