Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoning

TL;DR

This paper introduces Spatial Policy (SP), a novel spatial-aware visuomotor framework for robotic manipulation that improves task performance by explicit spatial modeling and reasoning, bridging visual plans to control in complex environments.

Contribution

The paper presents a unified spatial-aware visuomotor framework with explicit spatial modeling, flow-based action prediction, and a spatial reasoning feedback policy, advancing robotic manipulation capabilities.

Findings

Over 33% improvement on Meta-World tasks

Over 25% improvement on iTHOR tasks

Effective in real-world robotic experiments

Abstract

Vision-centric hierarchical embodied models have demonstrated strong potential. However, existing methods lack spatial awareness capabilities, limiting their effectiveness in bridging visual plans to actionable control in complex environments. To address this problem, we propose Spatial Policy (SP), a unified spatial-aware visuomotor robotic manipulation framework via explicit spatial modeling and reasoning. Specifically, we first design a spatial-conditioned embodied video generation module to model spatially guided predictions through the spatial plan table. Then, we propose a flow-based action prediction module to infer executable actions with coordination. Finally, we propose a spatial reasoning feedback policy to refine the spatial plan table via dual-stage replanning. Extensive experiments show that SP substantially outperforms state-of-the-art baselines, achieving over 33% improvement on Meta-World and over 25% improvement on iTHOR, demonstrating strong effectiveness across 23 embodied control tasks. We additionally evaluate SP in real-world robotic experiments to verify its practical viability. SP enhances the practicality of embodied models for robotic control applications. Code and checkpoints are maintained at https://plantpotatoonmoon.github.io/SpatialPolicy/.