Spatially Conditioned Diffusion Policy: Learning Precise and Robust Manipulation with a Single RGB Camera

• URL: http://arxiv.org/abs/2606.14535v1
• Date: Fri, 12 Jun 2026 15:12:03 GMT
• Title: Spatially Conditioned Diffusion Policy: Learning Precise and Robust Manipulation with a Single RGB Camera
• Abstract summary: Spatially Conditioned Diffusion Policy (SCDP) is a diffusion-based visuomotor policy that achieves precise and robust manipulation in a single-camera setting.<n>SCDP consists of two key components: (i) a visual encoder that produces multi-scale feature maps to capture both broader context and fine-grained visual features, and (ii) a spatial conditioning module that samples point-wise features along intermediate end-effector trajectories in the diffusion loop.
• Score: 6.648702147742411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent visual imitation learning systems have widely adopted multi-camera setups with wrist-mounted cameras as the de facto standard. However, manipulation from a single global view remains challenging, as the policy should capture fine-grained interaction details and identify task-relevant regions without local wrist views. To address this challenge, we present Spatially Conditioned Diffusion Policy (SCDP), a diffusion-based visuomotor policy that achieves precise and robust manipulation in a single-camera setting. Our key idea is that end-effector trajectories can serve as visual attention anchors that reflect task-relevant regions. Building on this idea, SCDP consists of two key components: (i) a visual encoder that produces multi-scale feature maps to capture both broader context and fine-grained visual features, and (ii) a spatial conditioning module that samples point-wise features along intermediate end-effector trajectories in the diffusion loop. Extensive simulation experiments show that SCDP consistently outperforms strong single-view baselines and achieves performance comparable to multi-camera baselines. Real-world experiments further demonstrate precise manipulation and robustness to visual distractors, highlighting the potential of single-camera imitation learning.

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