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Andreas Schaarschmidt

1 accepted papers

2021

Uncertainty-Aware Contact-Safe Model-Based Reinforcement Learning

RA-L 2021

This letter presents contact-safe Model-based Reinforcement Learning (MBRL) for robot applications that achieves contact-safe behaviors in the learning process. In typical MBRL, we cannot expect the data-driven model to generate accurate and reliable policies to the intended robotic tasks during the

Cited by 21SourceScholar