4 accepted papers
Meta-reinforcement learning offers a mechanism for zero-shot adaptation, enabling agents to handle new tasks with parametric variation in real-world environments. However, existing methods still struggle with task-level adaptation, which demands generalization beyond simple variations within tasks,
Zero-shot adaptation is crucial for agents facing new tasks. Offline Meta-Reinforcement Learning (OMRL), utilizing offline multi-task datasets to train policies, offers a way to attain this ability. Although most OMRL methods construct task representations via contrastive learning and merge them wit…
For a general-purpose robot, it is desirable to imitate human demonstration videos that can effectively solve long-horizon tasks and perform novel ones. Recent advances in skill-based imitation learning have shown that extracting skill embedding from raw human videos is a promising paradigm to enabl…
For autonomous robots, it is desirable to learn coordination of primitive skills that can effectively solve long-horizon tasks and perform novel ones. Recent advances in hierarchical policy learning have shown that decomposing complex tasks into sequences of primitive skills which are called sketche