3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots
This paper presents a hybrid framework combining Reinforcement Learning (RL) and Dynamic Window Approach (DWA) for adaptive 3D local navigation of high-degree-of-freedom robots. Using sparse point cloud data, it dynamically adjusts the motion and shape of a deformable microrobot to navigate toward goals in constrained environments while maximizing occupied volume. In 1080 simulated vascular network trials, the hybrid method significantly outperforms pure RL and model-based methods in deformation and navigation. The controller achieves near-perfect path completion and robustness in unseen scenarios, highlighting hybrid planning for efficient 3D navigation under sparse sensing.
[2605.12689] 3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots
[Submitted on 12 May 2026]
Title:3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots
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Abstract:In this paper, we present a novel hybrid approach that combines Reinforcement Learning (RL) with Dynamic Window Approach (DWA) for adaptive 3D local navigation of high-degree-of-freedom robotic systems. Our method leverages sparse point cloud data to dynamically adjust both the motion and the shape of a deformable microrobot, enabling the system to navigate toward a goal in complex, constrained environments while maximizing the occupied volume. We evaluate our framework in a simulated vascular network. Experimental results, based on 1080 trials, indicate that integrating RL with a DWA-based local planner significantly enhances both deformation and navigation capabilities compared to a pure RL and a model-based methods. In particular, the proposed autonomous controller consistently achieves high deformation and near-perfect path completion during training and maintains robust performance in unseen scenarios. These findings highlight the potential of hybrid planning strategies for efficient and adaptive 3D navigation under sparse sensory conditions.
Comments: Accepted to IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM2026
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.12689 [cs.RO]
(or arXiv:2605.12689v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.12689
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Enrico Turco [view email] [v1] Tue, 12 May 2026 19:37:32 UTC (3,158 KB)
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