arXiv Machine Learning
CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 16
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.
arXiv AI
Jun 3
AirDreamer: Generalist Drone Navigation with World Models
arXiv:2606. 03252v1 Announce Type: cross Abstract: Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental structure relative to the robot's capabilities.
arXiv AI
Jun 4
AgenticDiffusion: Agentic Diffusion-based Path Planning for Vision-Based UAV Navigation
arXiv:2606. 04111v1 Announce Type: cross Abstract: Indoor UAV navigation requires efficient exploration, scene understanding, and reliable trajectory execution under limited field-of-view observations.
arXiv AI
Aug 13
DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation
arXiv:2608. 12308v1 Announce Type: cross Abstract: Aerial vision-language navigation (VLN) requires an embodied agent to integrate visual evidence over time, plan future actions, and determine when it has reached a navigation goal under partial observability.
arXiv Machine Learning
Jun 17
Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned
arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.
arXiv AI
Jul 29
Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller
arXiv:2607. 25728v1 Announce Type: cross Abstract: This paper presents a cooperative indoor UAV guidance framework that combines a shared voxel-map world model with a multi-agent Soft Actor-Critic (MASAC) controller.