# Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds

arXiv: https://arxiv.org/abs/2010.14838 · HTML: https://mlsanity.com/paper/2010.14838/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation](https://mlsanity.com/paper/2410.10766/) (2024)
- [Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation](https://mlsanity.com/paper/2507.11001/) (2025)
- [PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2211.10714/) (2022)
- [Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2402.14569/) (2024)
- [Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control](https://mlsanity.com/paper/2210.10639/) (2022)
- [GUIDEd Agents: Enhancing Navigation Policies through Task-Specific Uncertainty Abstraction in Localization-Limited Environments](https://mlsanity.com/paper/2410.15178/) (2024)
- [Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements](https://mlsanity.com/paper/2505.13837/) (2025)
- [VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning](https://mlsanity.com/paper/2309.07832/) (2023)
- [DreamFlow: Local Navigation Beyond Observation via Conditional Flow Matching in the Latent Space](https://mlsanity.com/paper/2603.02976/) (2026)
- [3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots](https://mlsanity.com/paper/2605.12689/) (2026)
- [RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments](https://mlsanity.com/paper/2404.16672/) (2024)
- [SafeMove-RL: A Certifiable Reinforcement Learning Framework for Dynamic Motion Constraints in Trajectory Planning](https://mlsanity.com/paper/2505.12648/) (2025)
- [ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real](https://mlsanity.com/paper/2506.01759/) (2025)
- [Digital Twin Supervised Reinforcement Learning Framework for Autonomous Underwater Navigation](https://mlsanity.com/paper/2512.10925/) (2025)
- [KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility](https://mlsanity.com/paper/2606.12042/) (2026)
- [Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms](https://mlsanity.com/paper/2608.00625/) (2026)

## Abstract (arXiv)

We present a novel Deep Reinforcement Learning (DRL) based policy to compute dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) in terms of satisfying the robot's dynamics constraints with state-of-the-art DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the environmental obstacles' motions in a novel low-dimensional observation space. It also uses a novel reward function to positively reinforce velocities that move the robot away from the obstacle's heading direction leading to significantly lower number of collisions. We evaluate our method in realistic 3-D simulated environments and on a real differential drive robot in challenging dense indoor scenarios with several walking pedestrians. We compare our method with state-of-the-art collision avoidance methods and observe significant improvements in terms of success rate (up to 33% increase), number of dynamics constraint violations (up to 61% decrease), and smoothness. We also conduct ablation studies to highlight the advantages of our observation space formulation, and reward structure.