mlsanityData to 14 Oct 2026

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

arXiv 2505.12648 ↗First posted 19 May 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

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.

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.

Builds on

10 of 25 references.

References · 10

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2017PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planningcited by 69
  3. 2022Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graphcited by 53
  4. 2021Goal-Driven Autonomous Exploration Through Deep Reinforcement Learningcited by 35
  5. 2019RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policiescited by 20
  6. 2024TD3 Based Collision Free Motion Planning for Robot Navigationcited by 20
  7. 2020Dynamically Feasible Deep Reinforcement Learning Policy for Robot Navigation in Dense Mobile Crowdscited by 16
  8. 2022Motion Primitives-based Navigation Planning using Deep Collision Predictioncited by 11
  9. 2023Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigationcited by 10
  10. 2023Safe Hierarchical Navigation in Crowded Dynamic Uncertain Environmentscited by 2

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  1. 2025Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
  2. 2024A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
  3. 2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
  4. 2026Safety-aware Skill Adaptation for Reinforcement Learning in Dynamic Environments
  5. 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning
  6. 2026Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation
  7. 2021Safe Continuous Control with Constrained Model-Based Policy Optimization
  8. 2026Hamilton-Jacobi Reachability-Based Safe Reinforcement Learning for Emergency Collision Avoidance