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An annotation pipeline consisting of a diffusion model to lift 2D hand keypoint sequences to 4D hand motion and a diffusion loss to account for the multimodality in hand motion distribution is designed.
Across manipulation and reasoning tasks, PDE enables RL to learn successful policies even from zero-reward starts, and improves sample efficiency more broadly.
Learning from Hindsight is presented, which brings hindsight relabeling to RL post-training of VLAs by scoring failed rollouts against the tasks they actually achieved, and achieves 5 times improvement in sample efficiency, and outperforms a dense progress-reward baseline on out-of-distribution LIBERO-PRO tasks.
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