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SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring
In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation.
DIDA: Denoised Imitation Learning based on Domain Adaptation
Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications.
AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors
Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control.
SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models
Model-based imitation learning (MBIL) is a popular reinforcement learning method that improves sample efficiency on high-dimension input sources, such as images and videos.
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