Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control

• URL: http://arxiv.org/abs/2506.01943v2
• Date: Fri, 04 Jul 2025 04:06:12 GMT
• Title: Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control
• Authors: Xiao Fu, Xintao Wang, Xian Liu, Jianhong Bai, Runsen Xu, Pengfei Wan, Di Zhang, Dahua Lin,
• Abstract summary: We present RoboMaster, a novel framework that models inter-object dynamics through a collaborative trajectory formulation.<n>Unlike prior methods that decompose objects, our core is to decompose the interaction process into three sub-stages: pre-interaction, interaction, and post-interaction.<n>Our method outperforms existing approaches, establishing new state-of-the-art performance in trajectory-controlled video generation for robotic manipulation.
• Score: 72.00655365269
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in video diffusion models have demonstrated strong potential for generating robotic decision-making data, with trajectory conditions further enabling fine-grained control. However, existing trajectory-based methods primarily focus on individual object motion and struggle to capture multi-object interaction crucial in complex robotic manipulation. This limitation arises from multi-feature entanglement in overlapping regions, which leads to degraded visual fidelity. To address this, we present RoboMaster, a novel framework that models inter-object dynamics through a collaborative trajectory formulation. Unlike prior methods that decompose objects, our core is to decompose the interaction process into three sub-stages: pre-interaction, interaction, and post-interaction. Each stage is modeled using the feature of the dominant object, specifically the robotic arm in the pre- and post-interaction phases and the manipulated object during interaction, thereby mitigating the drawback of multi-object feature fusion present during interaction in prior work. To further ensure subject semantic consistency throughout the video, we incorporate appearance- and shape-aware latent representations for objects. Extensive experiments on the challenging Bridge V2 dataset, as well as in-the-wild evaluation, demonstrate that our method outperforms existing approaches, establishing new state-of-the-art performance in trajectory-controlled video generation for robotic manipulation.

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