mlsanityData to 14 Oct 2026

Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning

arXiv 2408.14063 ↗First posted 26 Aug 2024cs.RO cs.LGcited by 4

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

Abstract (arXiv)

Trajectory planning under kinodynamic constraints is fundamental for advanced robotics applications that require dexterous, reactive, and rapid skills in complex environments. These constraints, which may represent task, safety, or actuator limitations, are essential for ensuring the proper functioning of robotic platforms and preventing unexpected behaviors. Recent advances in kinodynamic planning demonstrate that learning-to-plan techniques can generate complex and reactive motions under intricate constraints. However, these techniques necessitate the analytical modeling of both the robot and the entire task, a limiting assumption when systems are extremely complex or when constructing accurate task models is prohibitive. This paper addresses this limitation by combining learning-to-plan methods with reinforcement learning, resulting in a novel integration of black-box learning of motion primitives and optimization. We evaluate our approach against state-of-the-art safe reinforcement learning methods, showing that our technique, particularly when exploiting task structure, outperforms baseline methods in challenging scenarios such as planning to hit in robot air hockey. This work demonstrates the potential of our integrated approach to enhance the performance and safety of robots operating under complex kinodynamic constraints.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 45 of its 63 references that are in the map. First posted on arXiv 26 Aug 2024.

Builds on

45 of 63 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications13 Apr

Other references · 44

  1. 2017Constrained Policy Optimizationcited by 983
  2. 2018Reward Constrained Policy Optimizationcited by 417
  3. 2019Control Barrier Functions: Theory and Applicationscited by 372
  4. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 366
  5. 2020Responsive Safety in Reinforcement Learning by PID Lagrangian Methodscited by 316
  6. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  7. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
  8. 2018Safe Exploration in Continuous Action Spacescited by 247
  9. 2020Projection-Based Constrained Policy Optimizationcited by 203
  10. 2017A General Safety Framework for Learning-Based Control in Uncertain Robotic Systemscited by 163
  11. 2023Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Modelscited by 161
  12. 2019Lyapunov-based Safe Policy Optimization for Continuous Controlcited by 145
32 more
  1. 2020Provably Efficient Safe Exploration via Primal-Dual Policy Optimizationcited by 134
  2. 2023Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learningcited by 133
  3. 2019IPO: Interior-point Policy Optimization under Constraintscited by 132
  4. 2017OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real Worldcited by 70
  5. 2019Learning for Safety-Critical Control with Control Barrier Functionscited by 69
  6. 2019Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Plannerscited by 66
  7. 2024Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Modelcited by 64
  8. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 59
  9. 2021Dynamic Movement Primitives in Robotics: A Tutorial Surveycited by 47
  10. 2022Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Riskcited by 41
  11. 2014Safe Exploration of State and Action Spaces in Reinforcement Learningcited by 38
  12. 2020Learning to Play Table Tennis From Scratch using Muscular Robotscited by 35
  13. 2020Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Controlcited by 28
  14. 2022Safe Reinforcement Learning Using Black-Box Reachability Analysiscited by 26
  15. 2020Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotioncited by 25
  16. 2021Differentiable Trust Region Layers for Deep Reinforcement Learningcited by 25
  17. 2022ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitivescited by 25
  18. 2021Specializing Versatile Skill Libraries using Local Mixture of Expertscited by 24
  19. 2022Deep Black-Box Reinforcement Learning with Movement Primitivescited by 20
  20. 2022Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interactioncited by 17
  21. 2020High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewardscited by 14
  22. 2021Motion Planning by Learning the Solution Manifold in Trajectory Optimizationcited by 13
  23. 2024Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learningcited by 12
  24. 2023Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Riskcited by 11
  25. 2021Efficient and Reactive Planning for High Speed Robot Air Hockeycited by 8
  26. 2022Athletic Mobile Manipulator System for Robotic Wheelchair Tenniscited by 8
  27. 2023Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformerscited by 8
  28. 2020Learning from Experience for Rapid Generation of Local Car Maneuverscited by 4
  29. 2020An analytical diabolo model for robotic learning and controlcited by 3
  30. 2022Controlling the Cascade: Kinematic Planning for N-ball Toss Jugglingcited by 3
  31. 2022Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path constructioncited by 2
  32. 2022Learning and Blending Robot Hugging Behaviors in Time and Spacecited by 2

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Modelscited by 33
  2. 2025BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learningcited by 27
  3. 2024TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learningcited by 19
  4. 2025MoRe-ERL: Learning Motion Residuals using Episodic Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
  2. 2025SafeMove-RL: A Certifiable Reinforcement Learning Framework for Dynamic Motion Constraints in Trajectory Planning
  3. 2026Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
  4. 2026Safety-aware Skill Adaptation for Reinforcement Learning in Dynamic Environments
  5. 2025Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planning
  6. 2025Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
  7. 2026Safe Execution of RL Policies Via Acceleration-Based CBF-QP Constraint Enforcement for Real-World Robotic Deployments
  8. 2021Safe Continuous Control with Constrained Model-Based Policy Optimization