Breaking long manipulation tasks into atomic subtasks and collecting demonstrations from varied starting poses improves imitation learning success using fewer demonstration frames.
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Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space
Breaking long manipulation tasks into atomic subtasks and collecting demonstrations from varied starting poses improves imitation learning success using fewer demonstration frames.