# Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation

arXiv: https://arxiv.org/abs/2512.24651 · HTML: https://mlsanity.com/paper/2512.24651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 31 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning](https://mlsanity.com/paper/2005.05420/) (2020)
- [Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07119/) (2021)
- [Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving](https://mlsanity.com/paper/2002.00434/) (2020)
- [Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning](https://mlsanity.com/paper/2408.14183/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Convergence Framework for Deep V-Learning: Error Propagation and Sharp Action-Gap Bounds](https://mlsanity.com/paper/2609.18782/) (2026)

## Abstract (arXiv)

Autonomous mobile robots operating in complex, dynamic environments face the dual challenge of navigating large-scale, structurally diverse spaces with static obstacles while safely interacting with various moving agents. Traditional graph-based planners excel at long-range pathfinding but lack reactivity, while Deep Reinforcement Learning (DRL) methods demonstrate strong collision avoidance but often fail to reach distant goals due to a lack of global context. We propose Hybrid Motion Planning with Deep Reinforcement Learning (HMP-DRL), a hybrid framework that bridges this gap. Our approach utilizes a graph-based global planner to generate a path, which is integrated into a local DRL policy via a sequence of checkpoints encoded in both the state space and reward function. To ensure social compliance, the local planner employs an entity-aware reward structure that dynamically adjusts safety margins and penalties based on the semantic type of surrounding agents. We validate the proposed method through extensive testing in a realistic simulation environment derived from real-world map data. Comprehensive experiments demonstrate that HMP-DRL consistently outperforms other methods, including state-of-the-art approaches, in terms of key metrics of robot navigation: success rate, collision rate, and time to reach the goal. Overall, these findings confirm that integrating long-term path guidance with semantically-aware local control significantly enhances both the safety and reliability of autonomous navigation in complex human-centric settings.