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

arXiv: https://arxiv.org/abs/2503.08479 · HTML: https://mlsanity.com/paper/2503.08479/

## Key facts

- Cited by 1 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 14 of its 27 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 14 of 14)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Adaptive Safety with Control Barrier Functions](https://mlsanity.com/paper/1910.00555/) (2019)
- [Benchmarking Metric Ground Navigation](https://mlsanity.com/paper/2008.13315/) (2020)
- [FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments](https://mlsanity.com/paper/2001.04420/) (2020)
- [FaSTrack: a Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking](https://mlsanity.com/paper/2102.07039/) (2021)
- [Safety-Critical Control and Planning for Obstacle Avoidance between Polytopes with Control Barrier Functions](https://mlsanity.com/paper/2109.12313/) (2021)
- [Learning a Better Control Barrier Function](https://mlsanity.com/paper/2205.05429/) (2022)
- [Rate-Tunable Control Barrier Functions: Methods and Algorithms for Online Adaptation](https://mlsanity.com/paper/2303.12966/) (2023)
- [Collision-free Source Seeking Control Methods for Unicycle Robots](https://mlsanity.com/paper/2212.07203/) (2022)
- [A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environments](https://mlsanity.com/paper/2402.01617/) (2024)
- [Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The 3rd BARN Challenge at ICRA 2024](https://mlsanity.com/paper/2407.01862/) (2024)

## Built on by (most cited first, 1 of 1)

- [Topology-Driven Anti-Entanglement Control for Soft Robots](https://mlsanity.com/paper/2605.05236/) (2026)

## 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.