mlsanityData to 14 Oct 2026

Bridging the Gap: Regularized Reinforcement Learning for Improved Classical Motion Planning with Safety Modules

arXiv 2403.18524 ↗First posted 27 Mar 2024cs.ROcited by 0

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

Abstract (arXiv)

Classical navigation planners can provide safe navigation, albeit often suboptimally and with hindered human norm compliance. ML-based, contemporary autonomous navigation algorithms can imitate more natural and humancompliant navigation, but usually require large and realistic datasets and do not always provide safety guarantees. We present an approach that leverages a classical algorithm to guide reinforcement learning. This greatly improves the results and convergence rate of the underlying RL algorithm and requires no human-expert demonstrations to jump-start the process. Additionally, we incorporate a practical fallback system that can switch back to a classical planner to ensure safety. The outcome is a sample efficient ML approach for mobile navigation that builds on classical algorithms, improves them to ensure human compliance, and guarantees safety.

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 9 of its 40 references that are in the map. First posted on arXiv 27 Mar 2024.

Builds on

9 of 40 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2016End to End Learning for Self-Driving Carscited by 1,009
  4. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 397
  5. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 319
  6. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 129
  7. 2021Core Challenges of Social Robot Navigation: A Surveycited by 106
  8. 2017Agile Autonomous Driving using End-to-End Deep Imitation Learningcited by 10
  9. 2018Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failurescited by 1

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  1. 2024A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
  2. 2026Imagine to Ensure Safety in Hierarchical Reinforcement Learning
  3. 2024Safe Policy Exploration Improvement via Subgoals
  4. 2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
  5. 2025Adaptive Dynamics Planning for Robot Navigation
  6. 2024Hybrid Classical/RL Local Planner for Ground Robot Navigation
  7. 2024Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning
  8. 2026Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation