# DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles

arXiv: https://arxiv.org/abs/2609.38873 · HTML: https://mlsanity.com/paper/2609.38873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Placed in

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

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
- [MuJoCo Playground](https://mlsanity.com/paper/2502.08844/) (2025)
- [Safe Reinforcement Learning Using Robust Control Barrier Functions](https://mlsanity.com/paper/2110.05415/) (2021)
- [Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures](https://mlsanity.com/paper/2505.00779/) (2025)
- [Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction](https://mlsanity.com/paper/2203.03749/) (2022)
- [CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning](https://mlsanity.com/paper/2403.18765/) (2024)
- [Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents](https://mlsanity.com/paper/2410.11671/) (2024)
- [CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions](https://mlsanity.com/paper/2510.14959/) (2025)
- [Collision-Free MPC for Legged Robots in Static and Dynamic Scenes](https://mlsanity.com/paper/2103.13987/) (2021)
- [Barrier Functions Inspired Reward Shaping for Reinforcement Learning](https://mlsanity.com/paper/2403.01410/) (2024)
- [SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics](https://mlsanity.com/paper/2505.11494/) (2025)
- [Scalable Multi-Objective Robot Reinforcement Learning through Gradient Conflict Resolution](https://mlsanity.com/paper/2509.14816/) (2025)
- [A Collision Cone Approach for Control Barrier Functions](https://mlsanity.com/paper/2403.07043/) (2024)
- [Learning to Adapt Control Barrier Functions Under Epistemic and Aleatoric Uncertainty](https://mlsanity.com/paper/2504.03038/) (2025)
- [Is Your Safe Controller Actually Safe? A Critical Review of CBF Tautologies and Hidden Assumptions](https://mlsanity.com/paper/2603.06954/) (2026)
- [PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball](https://mlsanity.com/paper/2607.28623/) (2026)
- [Safe Quadrotor Navigation using Composite Control Barrier Functions](https://mlsanity.com/paper/2502.04101/) (2025)
- [Beyond Collision Cones: Dynamic Obstacle Avoidance for Nonholonomic Robots via Dynamic Parabolic Control Barrier Functions](https://mlsanity.com/paper/2510.01402/) (2025)
- [DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions](https://mlsanity.com/paper/2605.21257/) (2026)

## Abstract (arXiv)

Robots operating in human-centered environments must safely navigate among multiple dynamic obstacles to avoid collisions with people and surrounding infrastructure. Control barrier functions (CBFs) provide an effective mechanism for safety filtering, and recent CBF-based reinforcement learning (RL) methods embed such safety information into learned policies. However, executing only safety-filtered actions during training can restrict policy exploration, a limitation that becomes particularly consequential in dynamic scenes where safety depends on relative robot-obstacle motion. We propose DODGER, a safety-guided RL framework that directly executes policy-generated actions to drive training rollouts while using CBF-filtered references and constraint violations to shape the policy toward collision-avoidance behavior. We evaluate DODGER through a Dubins-car safety analysis and demonstrate goal-directed navigation among multiple dynamic obstacles in full-order humanoid simulation and real-world humanoid experiments using LiDAR-based perception, without a runtime safety filter.