VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy
arXiv:2606.14879v1 Announce Type: cross Abstract: Mobile agents require efficient exploration strategies to map unseen environments and autonomously plan tasks. Traditional methods rely on generating occupancy maps and optimizing the sequence in which unexplored regions are visited. However, in sensor-constrained settings, such as those limited to monocular cameras, generating accurate occupancy maps is challenging. To address this, we propose VANDERER, an exploration framework that leverages a Visual Curiosity Module (VCM) to guide pre-trained diffusion policies using only monocular image dat
Advances in diffusion models and visual learning are enabling new approaches to autonomous exploration, particularly for sensor-constrained agents like monocular robots.
This development can significantly improve the efficiency and adaptability of robotic exploration and task planning in complex, unknown, and sensor-limited environments, which are common in real-world applications.
Traditional occupancy map-based exploration methods are being augmented or replaced by frameworks that leverage visual data and predictive policies, simplifying sensor requirements for mobile agents.
- · Robotics companies
- · Logistics and delivery sectors
- · AI hardware manufacturers
- · Defense and security (e.g., reconnaissance)
- · Manufacturers of complex multi-sensor suites
- · Developers of purely map-dependent navigation systems
More cost-effective and flexible autonomous mobile robots for various industrial and consumer applications.
Accelerated deployment of autonomous systems in challenging environments where high-fidelity mapping is difficult.
Shift in robotic design towards simpler, more visually-driven architectures, reducing overall complexity and cost.
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Read at arXiv cs.LG