Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction

• URL: http://arxiv.org/abs/2505.24156v1
• Date: Fri, 30 May 2025 03:01:21 GMT
• Title: Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction
• Authors: Chenyou Fan, Fangzheng Yan, Chenjia Bai, Jiepeng Wang, Chi Zhang, Zhen Wang, Xuelong Li,
• Abstract summary: Existing approaches rely on Vision-Language-Action (VLA) models to acquire bimanual policies.<n>We propose a novel bimanual foundation policy by fine-tuning the leading text-to-video models to predict robot trajectories.<n>Our method mitigates the ambiguity of language in single-stage text-to-video prediction and significantly reduces the robot-data requirement.
• Score: 47.86532300894681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning a generalizable bimanual manipulation policy is extremely challenging for embodied agents due to the large action space and the need for coordinated arm movements. Existing approaches rely on Vision-Language-Action (VLA) models to acquire bimanual policies. However, transferring knowledge from single-arm datasets or pre-trained VLA models often fails to generalize effectively, primarily due to the scarcity of bimanual data and the fundamental differences between single-arm and bimanual manipulation. In this paper, we propose a novel bimanual foundation policy by fine-tuning the leading text-to-video models to predict robot trajectories and training a lightweight diffusion policy for action generation. Given the lack of embodied knowledge in text-to-video models, we introduce a two-stage paradigm that fine-tunes independent text-to-flow and flow-to-video models derived from a pre-trained text-to-video model. Specifically, optical flow serves as an intermediate variable, providing a concise representation of subtle movements between images. The text-to-flow model predicts optical flow to concretize the intent of language instructions, and the flow-to-video model leverages this flow for fine-grained video prediction. Our method mitigates the ambiguity of language in single-stage text-to-video prediction and significantly reduces the robot-data requirement by avoiding direct use of low-level actions. In experiments, we collect high-quality manipulation data for real dual-arm robot, and the results of simulation and real-world experiments demonstrate the effectiveness of our method.

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