Reproduce “Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning” on OpenResearch
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About this paper
Yijun Liu, Yuwei Liu, Yuan Meng, Jieheng Zhang, Yuwei Zhou, Ye Li, Jiacheng Jiang, Kangye Ji and 3 more
A framework for visuomotor robotic manipulation from Tsinghua University integrates explicit spatial modeling and reasoning into a closed-loop perception-to-control pipeline, enhancing robot ability to generate physically plausible visual plans and execute reliable actions. This approach achieved an 86.7% success rate on the Meta-World benchmark and a 59.6% success rate on iTHOR navigation tasks, demonstrating superior performance and robustness in complex environments.
Key insights
- Explicitly encoding structured spatial intent via a VLM and a "spatial plan table" effectively guides video generation, resulting in physically plausible and task-aligned visual trajectories.
- Integrating optical flow as a conditioning signal within a diffusion policy robustly translates spatially consistent visual plans into precise, executable actions.
- A dual-stage feedback mechanism, combining VLM-based video validation and rule-based execution replanning, is critical for achieving robust, long-horizon control and dynamic error recovery.
Summary by alphaXiv.
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