aiqbench analysis
AirDreamer demonstrates a significant leap in drone autonomy by combining reinforcement learning with world models to navigate unseen environments without hand-crafted rules or deployment tuning. The approach achieves a 5.3% improvement in success rates over baselines while effectively transferring from simulation to real hardware, addressing critical generalization gaps in current robotics. This work highlights the viability of sparse reward functions and emergent behaviors for solving complex navigation tasks in cluttered, dynamic settings.
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. Previous methods, which assume the same environment configuration, often rely heavily on human-designed perception pipelines and predefined rules to guide the robot toward the target. This process is environment-dependent and generalizes poorly across environments. Inspired by animal navigation behavior, we design a navigation framework that navigates with a reinforcement-learning-based policy on top of a world-model-based environment understanding to overcome these issues. In addition, a sparse reward function without hand-crafted shaping terms is designed to avoid local minima traps and encourage yaw control behaviors. In simulation and on real drones, our method exhibits emergent capabilities for navigating complex, unseen environments and escaping local optima where other methods fail. In challenging maps, it achieves a 5.3% higher navigation success rate than best baseline. Furthermore, the proposed framework achieves effective sim-to-real transfer without any tuning during deployment. The code will be publicly available.
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. Previous methods, which assume the same environment configuration, often rely heavily on human-designed perception pipelines and predefined rules to guide the robot toward the target. This process is environment-dependent and generalizes poorly across environments. Inspired by animal navigation behavior, we design a navigation framework that navigates with a reinforcement-learning-based policy on top of a world-model-based environment understanding to overcome these issues. In addition, a sparse reward function without hand-crafted shaping terms is designed to avoid local minima traps and encourage yaw control behaviors. In simulation and on real drones, our method exhibits emergent capabilities for navigating complex, unseen environments and escaping local optima where other methods fail. In challenging maps, it achieves a 5.3% higher navigation success rate than best baseline. Furthermore, the proposed framework achieves effective sim-to-real transfer without any tuning during deployment. The code will be publicly available.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
I.2.9 (Primary), I.2.10 (Secondary)
arXiv:2606.03252 [cs.RO]
(or arXiv:2606.03252v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.03252 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 265 words