RL-100 framework helps robots refine learned tasks amid real-world disruptions

Robots are gradually making their way into a variety of settings, ranging from households to public spaces, offices, factories and health care facilities. Despite their potential, however, many existing robots do not perform as well in dynamic and unpredictable real-world environments as they do during controlled laboratory tests.

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