Learning-Based Navigation for Indoor Mobile Robots
This paper presents a learning-based navigation framework for indoor mobile robots that combines a supervised neural global planner with a learning-based DWA local planner refined by PPO. Experiments in simulation and real environments show feasible global routes and reliable local motion commands for safe obstacle avoidance. Source code will be released.
[2605.30468] Learning-Based Navigation for Indoor Mobile Robots
[Submitted on 28 May 2026]
Title:Learning-Based Navigation for Indoor Mobile Robots
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Abstract:This paper presents a learning-based navigation framework for indoor mobile robots. The proposed method combines a supervised neural global planner, trained from cost-aware A* expert trajectories, with the proposed Learning-Based DWA local planner, which is formulated as discrete candidate selection over the Dynamic Window Approach (DWA) action lattice. For local planning, the policy is first trained by behavior cloning and then refined by Proximal Policy Optimization (PPO) under feasibility-aware masking. The framework is implemented and evaluated in both simulated and real-world indoor environments. Experimental results show that the proposed method generates feasible global routes and reliable local motion commands for safe goal-directed navigation in the presence of obstacles. These results demonstrate the effectiveness of integrating learning-based global planning with reinforcement-learning-refined local control for indoor mobile robot navigation. The source code will be released at this https URL.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.30468 [cs.RO]
(or arXiv:2605.30468v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.30468
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tien-Dat Nguyen [view email] [v1] Thu, 28 May 2026 18:40:52 UTC (7,997 KB)
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