A new training approach for robot navigation allows multiple collisions per episode before reset, accelerating early learning and improving success rates over traditional single-collision resets.
Dwa-rl: Dynamically feasible deep reinforcement learning policy for robot navigation among mobile obstacles,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A hybrid RL-DWA controller achieves high deformation and near-perfect path completion for deformable microrobots navigating simulated 3D vascular networks from sparse point clouds.
citing papers explorer
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Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation
A new training approach for robot navigation allows multiple collisions per episode before reset, accelerating early learning and improving success rates over traditional single-collision resets.
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3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots
A hybrid RL-DWA controller achieves high deformation and near-perfect path completion for deformable microrobots navigating simulated 3D vascular networks from sparse point clouds.