mlsanityData to 14 Oct 2026

Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environments

arXiv 2503.08479 ↗First posted 11 Mar 2025cs.RO cs.LGcited by 1

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

Abstract (arXiv)

Motion planning failures during autonomous navigation often occur when safety constraints are either too conservative, leading to deadlocks, or too liberal, resulting in collisions. To improve robustness, a robot must dynamically adapt its safety constraints to ensure it reaches its goal while balancing safety and performance measures. To this end, we propose a Soft Actor-Critic (SAC)-based policy for adapting Control Barrier Function (CBF) constraint parameters at runtime, ensuring safe yet non-conservative motion. The proposed approach is designed for a general high-level motion planner, low-level controller, and target system model, and is trained in simulation only. Through extensive simulations and physical experiments, we demonstrate that our framework effectively adapts CBF constraints, enabling the robot to reach its final goal without compromising safety.

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 14 of its 27 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

14 of 27 references.

References · 14

  1. 2016OpenAI Gymcited by 1,736
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  3. 2019Control Barrier Functions: Theory and Applicationscited by 372
  4. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 287
  5. 2019Adaptive Safety with Control Barrier Functionscited by 25
  6. 2020Benchmarking Metric Ground Navigationcited by 20
  7. 2020FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environmentscited by 15
  8. 2021FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Trackingcited by 15
  9. 2021Safety-Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functionscited by 7
  10. 2022Learning a Better Control Barrier Functioncited by 6
  11. 2023Rate-Tunable Control Barrier Functions: Methods and Algorithms for Online Adaptationcited by 2
  12. 2022Collision-free Source Seeking Control Methods for Unicycle Robotscited by 1
2 more
  1. 2024A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environmentscited by 1
  2. 2024Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The 3rd BARN Challenge at ICRA 2024cited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Topology-Driven Anti-Entanglement Control for Soft Robotscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation ↗
  2. 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation
  3. 2025Certificated Actor-Critic: Hierarchical Reinforcement Learning with Control Barrier Functions for Safe Navigation
  4. 2025Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
  5. 2025A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
  6. 2026Toward Integrating Adaptive Experience Replay and Online Uncertainty Estimation in Safe Actor-Critic Optimal Control
  7. 2026Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation
  8. 2026Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning