arXiv:2606.31919v1 Announce Type: cross Abstract: Zero-shot Object Goal Navigation (ZSON) with RGB-only perception poses a fundamental challenge for embodied agents, as the absence of explicit depth information introduces severe physical uncertainty and semantic-physical misalignment. Existing approaches either rely on high-level semantic reasoning without geometric grounding or learn end-to-end policies that lack explicit physical constraints, often resulting in semantically plausible but physically unsafe behaviors. In this paper, we propose MVP-Nav, a physical-aware RGB-only navigation fram
The increasing sophistication of AI models and robotic platforms necessitates more robust and reliable navigation solutions, especially in perception-limited environments.
This development addresses a critical gap in embodied AI by enabling more physically aware and safer navigation for agents with limited sensory input, essential for real-world deployment.
The ability of RGB-only embodied agents to perform complex navigation tasks with greater safety and reliability becomes more feasible, reducing reliance on expensive sensor arrays.
- · Robotics companies
- · AI agents developers
- · Logistics and delivery sectors
- · Defence and exploration industries
- · Companies reliant solely on high-cost LiDAR/depth sensor solutions for navigatio
- · Less robust, purely semantic navigation approaches
Embodied AI applications, like robotic delivery or assistive robots, will see improved performance and safety in diverse environments.
Reduced hardware costs for autonomous systems could accelerate the adoption of robotics in various commercial and consumer sectors.
More capable and affordable robotic agents may begin to fill labor gaps in physically demanding or hazardous environments, leading to economic restructuring.
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Read at arXiv cs.AI