Translation as a Bridging Action: Transferring Manipulation Skills from Humans to Robots

Hugging Face Daily Papers Papers
robot-learning manipulation-skills vision-language-action human-to-robot transfer-learning bi-manual-robots attention-masking

Summary

This paper proposes a bridging action representation based on relative wrist translation in the head-camera frame to transfer human manipulation skills to bi-manual robots, using a vision-language-action model with interleaved action tokens and attention masking to handle embodiment differences.

We study whether we can learn novel manipulation skills from human actions to a bi-manual robot with parallel grippers. Human action data is cheap, abundant, and diverse, making it one of the most promising resources for scaling up robot learning. Yet transferring skills from humans to robots remains hard: most prior work treats humans as just another bi-manual 6DoF embodiment, where hand-pose estimates are noisy and the contact patterns of human fingers differ fundamentally from those of a parallel gripper. We argue that learning rotation-inclusive action signals from human data is therefore sub-optimal, and instead propose a bridging action representation: the relative wrist translation within the initial head-camera frame, an action space shared by humans and robots. To handle the potential absence of certain action components in different embodiments, we build a π_0-like vision-language-action model with interleaved action tokens and attention masking. On a suite of novel bi-manual manipulation tasks, our bridging action transfers human manipulation knowledge to robots far more effectively than noisy 6DoF human actions and scales with the amount of human data.
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Paper page - Translation as a Bridging Action: Transferring Manipulation Skills from Humans to Robots

Source: https://huggingface.co/papers/2606.28133

Abstract

Human manipulation skills are transferred to robots more effectively by using a bridging action representation based on relative wrist translation in the initial head-camera frame, combined with a vision-language-action model that handles embodiment differences through interleaved action tokens and attention masking.

We study whether we can learn novel manipulation skills from human actions to a bi-manual robot withparallel grippers. Human action data is cheap, abundant, and diverse, making it one of the most promising resources for scaling up robot learning. Yet transferring skills from humans to robots remains hard: most prior work treats humans as just another bi-manual 6DoF embodiment, where hand-pose estimates are noisy and the contact patterns of human fingers differ fundamentally from those of a parallel gripper. We argue that learning rotation-inclusive action signals from human data is therefore sub-optimal, and instead propose a bridging action representation: therelative wrist translationwithin the initialhead-camera frame, an action space shared by humans and robots. To handle the potential absence of certain action components in different embodiments, we build a π_0-likevision-language-action modelwithinterleaved action tokensandattention masking. On a suite of novelbi-manual manipulationtasks, our bridging action transfers human manipulation knowledge to robots far more effectively than noisy 6DoF human actions and scales with the amount of human data.

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