# SafeMove-RL: A Certifiable Reinforcement Learning Framework for Dynamic Motion Constraints in Trajectory Planning

arXiv: https://arxiv.org/abs/2505.12648 · HTML: https://mlsanity.com/paper/2505.12648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 19 May 2025.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 10 of 10)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07119/) (2021)
- [RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies](https://mlsanity.com/paper/1907.04799/) (2019)
- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowds](https://mlsanity.com/paper/2010.14838/) (2020)
- [Motion Primitives-based Navigation Planning using Deep Collision Prediction](https://mlsanity.com/paper/2201.03254/) (2022)
- [Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation](https://mlsanity.com/paper/2301.00362/) (2023)
- [Safe Hierarchical Navigation in Crowded Dynamic Uncertain Environments](https://mlsanity.com/paper/2303.14278/) (2023)

## Abstract (arXiv)

This study presents a dynamic safety margin-based reinforcement learning framework for local motion planning in dynamic and uncertain environments. The proposed planner integrates real-time trajectory optimization with adaptive gap analysis, enabling effective feasibility assessment under partial observability constraints. To address safety-critical computations in unknown scenarios, an enhanced online learning mechanism is introduced, which dynamically corrects spatial trajectories by forming dynamic safety margins while maintaining control invariance. Extensive evaluations, including ablation studies and comparisons with state-of-the-art algorithms, demonstrate superior success rates and computational efficiency. The framework's effectiveness is further validated on both simulated and physical robotic platforms.