aiqbench analysis
EmbodiedDiffusion advances autonomous navigation by integrating traversability estimation and trajectory planning into a single diffusion model, eliminating the need for separate planners or heavy mapping. By distilling semantics from Vision-Language Models and using embodiment conditioning, it enables real-time inference on heterogeneous platforms like quadrupeds and aerial drones with rapid adaptation to new environments. This approach addresses key bottlenecks in deployment time and cross-platform transferability for embodied AI systems.
EmbodiedDiffusion: End-to-End Traversability-Guided Visual Diffusion for Heterogeneous Robot Navigation
arXiv:2512.02851v5 Announce Type: replace Abstract: Visual traversability estimation is central to autonomous navigation, yet most approaches either rely on prompt-driven Vision-Language Model (VLM) or decouple traversability from trajectory planning, requiring separate planners with heavy mapping, manual tuning, and extended deployment time. We propose EmbodiedDiffusion, a diffusion-based framework that simultaneously predicts traversability maps and generates feasible trajectories from RGB images using planner-free synthetic supervision and embodiment conditioning for cross-platform transfer. The framework distills category-level traversability semantics from a VLM teacher into a lightweight student model during training, enabling prompt-free, real-time inference at deployment. A modular FiLM-based conditioning mechanism isolates embodiment-specific reasoning into a compact trainable subset of the network, allowing rapid adaptation to new robot platforms without retraining the visual backbone or the trajectory diffusion model. Across indoor environments with quadruped and aerial robots, EmbodiedDiffusion achieves 80-100% navigation success in the full-data regime with real-time inference (90 ms) and adapts to new platforms using only 10 min of visual data collection, demonstrating scalable, unified traversability reasoning and trajectory generation for heterogeneous robots.
Abstract:Visual traversability estimation is central to autonomous navigation, yet most approaches either rely on prompt-driven Vision-Language Model (VLM) or decouple traversability from trajectory planning, requiring separate planners with heavy mapping, manual tuning, and extended deployment time. We propose EmbodiedDiffusion, a diffusion-based framework that simultaneously predicts traversability maps and generates feasible trajectories from RGB images using planner-free synthetic supervision and embodiment conditioning for cross-platform transfer. The framework distills category-level traversability semantics from a VLM teacher into a lightweight student model during training, enabling prompt-free, real-time inference at deployment. A modular FiLM-based conditioning mechanism isolates embodiment-specific reasoning into a compact trainable subset of the network, allowing rapid adaptation to new robot platforms without retraining the visual backbone or the trajectory diffusion model. Across indoor environments with quadruped and aerial robots, EmbodiedDiffusion achieves 80-100% navigation success in the full-data regime with real-time inference (90 ms) and adapts to new platforms using only 10 min of visual data collection, demonstrating scalable, unified traversability reasoning and trajectory generation for heterogeneous robots.
Robotics (cs.RO)
arXiv:2512.02851 [cs.RO]
(or arXiv:2512.02851v5 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2512.02851 Focus to learn more arXiv-issued DOI via DataCite
IEEE Robotics and Automation Letters, vol. 11, no. 10, pp. 11697-11704, Oct. 2026
https://doi.org/10.1109/LRA.2026.3726345 Focus to learn more DOI(s) linking to related resources
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