mlsanityData to 7 Oct 2026

Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction

arXiv 2209.13308 ↗First posted 27 Sep 2022cs.RO cs.LGcited by 15

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Abstract (arXiv)

Safety is a crucial property of every robotic platform: any control policy should always comply with actuator limits and avoid collisions with the environment and humans. In reinforcement learning, safety is even more fundamental for exploring an environment without causing any damage. While there are many proposed solutions to the safe exploration problem, only a few of them can deal with the complexity of the real world. This paper introduces a new formulation of safe exploration for reinforcement learning of various robotic tasks. Our approach applies to a wide class of robotic platforms and enforces safety even under complex collision constraints learned from data by exploring the tangent space of the constraint manifold. Our proposed approach achieves state-of-the-art performance in simulated high-dimensional and dynamic tasks while avoiding collisions with the environment. We show safe real-world deployment of our learned controller on a TIAGo++ robot, achieving remarkable performance in manipulation and human-robot interaction tasks.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 39 of its 60 references that are in the map. First posted on arXiv 27 Sep 2022.

Builds on

39 of 60 references.

References · 39

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27 more
  1. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  2. 2020Recovery RL: Safe Reinforcement Learning with Learned Recovery Zonescited by 156
  3. 2017A General Safety Framework for Learning-Based Control in Uncertain Robotic Systemscited by 150
  4. 2019Lyapunov-based Safe Policy Optimization for Continuous Controlcited by 140
  5. 2020Provably Efficient Safe Exploration via Primal-Dual Policy Optimizationcited by 131
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  8. 2017Uncertainty-Aware Reinforcement Learning for Collision Avoidancecited by 86
  9. 2018Safe Reinforcement Learning with Model Uncertainty Estimatescited by 70
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  22. 2022Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environmentscited by 16
  23. 2015Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regretcited by 15
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 9; 2025: 1; 2026: 3.

15 papers.

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  1. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  2. 2024Distributionally Robust Constrained Reinforcement Learning under Strong Dualitycited by 15
  3. 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applicationscited by 12
  4. 2024Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controllercited by 5
  5. 2022Hierarchical Policy Blending As Optimal Transportcited by 4
  6. 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learningcited by 4
  7. 2024ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planningcited by 4
  8. 2024Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approachcited by 3
  9. 2025Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Taskscited by 3
  10. 2023Deep reinforcement learning for the dynamic vehicle dispatching problem: An event-based approachcited by 2
  11. 2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learningcited by 2
  12. 2024A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environmentscited by 0
3 more
  1. 2026V2P-Manip: Learning Dexterous Manipulation from Monocular Human Videoscited by 0
  2. 2026Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Controlcited by 0
  3. 2026Directional Constraints for Efficient Exploration in Safe Reinforcement Learningcited by 0