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About this paper
Hong Chen, Daqi Liu, Zehan Zhang, Haiguang Wang, Tianhao Lu, Longfei Yan, Haiyang Sun, Fangzhen Li and 5 more
The Spatial-perceiving World Action Model (SWAM) is introduced as a latent diffusion model for embodied visual navigation, jointly generating RGBD observation sequences and corresponding action trajectories from start and goal RGB inputs. SWAM achieved up to 75.1% lower Absolute Trajectory Error (ATE) and significantly higher inference efficiency compared to previous two-stage planning baselines, demonstrating strong zero-shot generalization capabilities across diverse environments.
Key insights
- Jointly generating visual (RGB-D) and action sequences within a single task-centric framework significantly improves trajectory accuracy and spatial consistency compared to verification-centric, two-stage planning methods.
- Integrating depth pseudo-labels during training, even with monocular RGB input at inference, provides crucial geometric priors that enhance the physical plausibility and structural integrity of predicted trajectories and observations.
- Specific regularization techniques like Trajectory-Scale Regularization (TSR) and the Visual-Guided Action Refinement (VGAR) module are essential for maintaining stable, accurate long-horizon predictions and for aligning generated actions with visual evidence.
Summary by alphaXiv.
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