arXiv:2606.29908v1 Announce Type: cross Abstract: Existing world model-based planners for visual navigation typically follow a verification-centric paradigm, decoupling goal intent from trajectory synthesis. This approach suffers from candidate dependence, heavy computational overhead, and inconsistencies between sampled actions and predicted visuals. To address these issues, we propose SWAM (Spatial-perceiving World Action Model), a task-centric joint observation-action generation framework. Given start and goal RGB observations, SWAM performs single-pass inference to simultaneously generate
The rapid advancement in visual navigation and world model-based planning necessitates more efficient and integrated solutions for embodied AI systems.
This development represents a significant step towards more computationally efficient and robust AI for physical navigation, impacting robotics and autonomous systems.
The SWAM framework's ability to perform joint observation-action generation in a single pass reduces computational overhead and improves consistency, making embodied navigation more practical.
- · Robotics companies
- · AI hardware manufacturers
- · Logistics and automation sectors
- · Defense contractors
- · Developers of legacy verification-centric navigation systems
- · Companies reliant on high-latency autonomous systems
More efficient and reliable embodied AI for tasks such as delivery, exploration, and industrial automation.
Acceleration of commercialization pathways for general-purpose humanoid robots and autonomous vehicles due to improved navigation capabilities.
Reduced operational costs and increased adoption of autonomous systems across various industries, potentially leading to significant labor displacement in blue-collar sectors.
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Read at arXiv cs.AI