DiffusionAnything: End-to-End In-context Diffusion Learning for Unified Navigation and Pre-Grasp Motion

• URL: http://arxiv.org/abs/2603.26322v1
• Date: Fri, 27 Mar 2026 11:40:13 GMT
• Title: DiffusionAnything: End-to-End In-context Diffusion Learning for Unified Navigation and Pre-Grasp Motion
• Abstract summary: Recent vision-language-action (VLA) models infer actions directly from visual input but demand massive computational resources.<n>We present a unified image-space diffusion policy handling both meter-scale navigation and centimeter-scale manipulation.<n>The model achieves robust zero-shot generalization to novel scenes while remaining suitable for onboard deployment.
• Score: 2.061143628317803
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Efficiently predicting motion plans directly from vision remains a fundamental challenge in robotics, where planning typically requires explicit goal specification and task-specific design. Recent vision-language-action (VLA) models infer actions directly from visual input but demand massive computational resources, extensive training data, and fail zero-shot in novel scenes. We present a unified image-space diffusion policy handling both meter-scale navigation and centimeter-scale manipulation via multi-scale feature modulation, with only 5 minutes of self-supervised data per task. Three key innovations drive the framework: (1) Multi-scale FiLM conditioning on task mode, depth scale, and spatial attention enables task-appropriate behavior in a single model; (2) trajectory-aligned depth prediction focuses metric 3D reasoning along generated waypoints; (3) self-supervised attention from AnyTraverse enables goal-directed inference without vision-language models and depth sensors. Operating purely from RGB input (2.0 GB memory, 10 Hz), the model achieves robust zero-shot generalization to novel scenes while remaining suitable for onboard deployment.

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