BATCH: Behavior Alignment for Cross-Embodiment Learning from Humans
Overview
This research presents BATCH, a framework that aligns human and robot behaviors using self-supervised temporal representations and cycle-consistency objectives. It employs an autoregressive video-action diffusion model with behavior-aligned co-training and human-to-robot style transfer to improve generalization on unseen task configurations in real-world manipulation tasks compared to unaligned data and prior baselines.
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- arXiv Robotics — research abstractsSignalBATCH: Behavior Alignment for Cross-Embodiment Learning from Humans
This research presents BATCH, a framework that aligns human and robot behaviors using self-supervised temporal representations and cycle-consistency objectives. It employs an autoregressive video-action diffusion model with behavior-aligned co-training and human-to-robot style transfer to improve generalization on unseen task configurations in real-world manipulation tasks compared to unaligned data and prior baselines.
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