USN: A Robust Imitation Learning Method against Diverse Action Noise
Xingrui Yu,
Bo Han,
Ivor W. Tsang
Abstract:Learning from imperfect demonstrations is a crucial challenge in imitation learning (IL). Unlike existing works that still rely on the enormous effort of expert demonstrators, we consider a more cost-effective option for obtaining a large number of demonstrations. That is, hire annotators to label actions for existing image records in realistic scenarios. However, action noise can occur when annotators are not domain experts or encounter confusing states. In this work, we introduce two particular forms of acti… Show more
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