4 accepted papers
Unsupervised skill discovery acquires a diverse repertoire of skills through intrinsic motivation, offering the potential to alleviate the labor-intensive reward engineering in reinforcement learning and the reliance on costly task-specific data in imitation learning. However, such methods typically…
Diffusion Policy (DP) is effective for imitation learning in robotic manipulation, yet likelihood-based replanning methods lack self-correction and often fail once execution deviates. Vision-Language Models (VLMs) offer strong spatial reasoning for failure handling but incur prohibitive latency for
Learning latent actions from large-scale videos is crucial for the pre-training of scalable embodied foundation models, yet existing methods often struggle with action-irrelevant distractors. Although incorporating action supervision can alleviate these distractions, its effectiveness is restricted
Imitation learning attracts much attention for its ability to allow robots to quickly learn human manipulation skills through demonstrations. However, in the real world, human demonstrations often exhibit random behavior that is not intended by humans. Collecting high-quality human datasets is both