CAVEAT, a diffusion policy conditioned on a recurrent internal state updated from fused LiDAR, visual, and pose features and trained from trajectories generated by the map-based FUELv2 expert, is investigated through simulation.
Abstract
Can exploratory UAV waypoint sequences be generated from multimodal onboard observations and a fixed-dimensional recurrent internal state without maintaining a persistent global map in the deployed policy? We investigate this question through CAVEAT, a diffusion policy conditioned on a recurrent internal state updated from fused LiDAR, visual, and pose features and trained from trajectories generated by the map-based FUELv2 expert. Rolling inference partially warm-starts consecutive predictions, while a temporary local signed distance field provides heuristic obstacle guidance. Simulation results evaluate both inference mechanisms and compare CAVEAT with its demonstration-generating expert. Proof-of-concept experiments on a Flyability Elios 3 demonstrate partial exploration of a previously unseen indoor environment and target-directed visual servoing using a separately trained policy.
LiDAR-based unmanned aerial vehicle (UAV) exploration builds maps by continually selecting where to observe next. However, decisions based on the measured map provide limited foresight into spatial continuations behind occlusions, leaving potentially informative directions unrecognized. We present WOLF, a world-model-g...
Yu-Yang Tian, Peng Yang, Peng-Yuan Wu et al.· 0 citations
In this paper, we propose AeroDiffusion, a real-time closed-loop diffusion planning framework for quadrotor navigation in dynamic environments. The core idea is to turn conditional diffusion from a single-shot trajectory generator into an onboard receding-horizon planner: temporal depth-state conditioning models multi-...
Hao-Wen Wang, Shu-Ting Zhang, Xin Meng et al.· IEEE Robotics and Automation...· 1 citation
Autonomous navigation in unknown, complex indoor environments remains challenging due to limited sensing range and severe partial observability. Conventional methods rely on local maps without foresight, causing dead-ends and long detours, while local goal selection based on Euclidean distance or frontier coverage fail...
Hong-Yu Song, Yun-Fang Ren, Ji-Gui Miao et al.· IEEE Robotics and Automation...· 0 citations
Autonomous navigation in real-world public service, industrial inspection, and emergency response often faces frequent changes in nominally static scene structures, which can quickly invalidate pre-built global maps and naturally lead to a mapless navigation setting. We propose an end-to-end 3D LiDAR based navigation f...
Yue Zhai, Yan-Zi Miao· IEEE Robotics and Automation...· 0 citations
We propose NUDGE (Nudge Update via Differentiable GEometry), a training-free obstacle-avoidance procedure that can be incorporated in any robot policy based on diffusion or flow matching, including diffusion policies and vision-language-action models. Our work injects gradients from a signed distance field, a function...
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Xingrong Diao, Zhi-Qiang Sun, Jian-Wei Peng et al.· IEEE Transactions on Automat...· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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