mlsanityData to 14 Oct 2026

Safe Reinforcement Learning Filter for Multicopter Collision-Free Tracking under disturbances

arXiv 2410.06852 ↗First posted 9 Oct 2024cs.ROcited by 1

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

Abstract (arXiv)

This paper proposes a safe reinforcement learning filter (SRLF) to realize multicopter collision-free trajectory tracking with input disturbance. A novel robust control barrier function (RCBF) with its analysis techniques is introduced to avoid collisions with unknown disturbances during tracking. To ensure the system state remains within the safe set, the RCBF gain is designed in control action. A safety filter is introduced to transform unsafe reinforcement learning (RL) control inputs into safe ones, allowing RL training to proceed without explicitly considering safety constraints. The SRLF obtains rigorous guaranteed safe control action by solving a quadratic programming (QP) problem that incorporates forward invariance of RCBF and input saturation constraints. Both simulation and real-world experiments on multicopters demonstrate the effectiveness and excellent performance of SRLF in achieving collision-free tracking under input disturbances and saturation.

Key facts. Cited by 1 paper 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 15 of its 31 references that are in the map. First posted on arXiv 9 Oct 2024.

Builds on

15 of 31 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2018Reward Constrained Policy Optimizationcited by 417
  4. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
  5. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 287
  6. 2018Safe Exploration in Continuous Action Spacescited by 247
  7. 2018A predictive safety filter for learning-based control of constrained nonlinear dynamical systemscited by 118
  8. 2020Learning to Walk in the Real World with Minimal Human Effortcited by 111
  9. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  10. 2023State-wise Safe Reinforcement Learning: A Surveycited by 55
  11. 2022Towards Safe Reinforcement Learning with a Safety Editor Policycited by 30
  12. 2018Input-to-State Safety With Control Barrier Functionscited by 15
3 more
  1. 2023Contrastive Initial State Buffer for Reinforcement Learningcited by 8
  2. 2022Conservative Distributional Reinforcement Learning with Safety Constraintscited by 4
  3. 2024Quadrotor Stabilization with Safety Guarantees: A Universal Formula Approachcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibilitycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
  2. 2024Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
  3. 2024Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents
  4. 2026Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning
  5. 2024Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning
  6. 2026Safe Execution of RL Policies Via Acceleration-Based CBF-QP Constraint Enforcement for Real-World Robotic Deployments
  7. 2025Safe Learning Control with Optimality and Stability Guarantees
  8. 2025CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions