mlsanityData to 13 Oct 2026

Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control

arXiv 2011.08421 ↗First posted 17 Nov 2020cs.RO cs.SY eess.SYcited by 28

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Abstract (arXiv)

Reinforcement Learning (RL) algorithms have achieved remarkable performance in decision making and control tasks due to their ability to reason about long-term, cumulative reward using trial and error. However, during RL training, applying this trial-and-error approach to real-world robots operating in safety critical environment may lead to collisions. To address this challenge, this paper proposes a Reachability-based Trajectory Safeguard (RTS), which leverages reachability analysis to ensure safety during training and operation. Given a known (but uncertain) model of a robot, RTS precomputes a Forward Reachable Set of the robot tracking a continuum of parameterized trajectories. At runtime, the RL agent selects from this continuum in a receding-horizon way to control the robot; the FRS is used to identify if the agent's choice is safe or not, and to adjust unsafe choices. The efficacy of this method is illustrated on three nonlinear robot models, including a 12-D quadrotor drone, in simulation and in comparison with state-of-the-art safe motion planning methods.

Key facts. Cited by 28 papers in the map (data through 13 Oct 2026). First posted on arXiv 17 Nov 2020.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 4; 2024: 8; 2025: 3; 2026: 4.

28 papers.

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  1. 2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 54
  2. 2023State-wise Safe Reinforcement Learning: A Surveycited by 48
  3. 2022Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarkingcited by 31
  4. 2024HardNet: Hard-Constrained Neural Networks with Universal Approximation Guaranteescited by 31
  5. 2022Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopescited by 23
  6. 2022Safe Reinforcement Learning Using Black-Box Reachability Analysiscited by 22
  7. 2022Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environmentscited by 16
  8. 2022Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interactioncited by 15
  9. 2021A Simple and Efficient Sampling-based Algorithm for General Reachability Analysiscited by 12
  10. 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applicationscited by 12
  11. 2024RAIL: Reachability-Aided Imitation Learning for Safe Policy Executioncited by 5
  12. 2025From Demonstrations to Safe Deployment: Path-Consistent Safety Filtering for Diffusion Policiescited by 5
16 more
  1. 2022Safe Reinforcement Learning using Data-Driven Predictive Controlcited by 4
  2. 2023Safe Reinforcement Learning with Dead-Ends Avoidance and Recoverycited by 4
  3. 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learningcited by 4
  4. 2021Risk Conditioned Neural Motion Planningcited by 3
  5. 2024Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approachcited by 3
  6. 2024Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo Theories and Reachability Analysiscited by 3
  7. 2025Geometry of Neural Reinforcement Learning in Continuous State and Action Spacescited by 3
  8. 2026X-Safe: Embodiment-Agnostic Safeguarding for Robot Arm Manipulation Policies via Action Maskingcited by 2
  9. 2024Do No Harm: A Counterfactual Approach to Safe Reinforcement Learningcited by 1
  10. 2025Predictive Safety Shield for Dyna-Q Reinforcement Learningcited by 1
  11. 2026Dyna-Style Safety Augmented Reinforcement Learning: Staying Safe in the Face of Uncertaintycited by 1
  12. 2023Safe Deep RL for Intraoperative Planning of Pedicle Screw Placementcited by 0
  13. 2023Adaptive Policy Learning to Additional Taskscited by 0
  14. 2024Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfactioncited by 0
  15. 2026Directional Constraints for Efficient Exploration in Safe Reinforcement Learningcited by 0
  16. 2026No Free Checker: A Survey of Verifiers for Robot Policiescited by 0