mlsanityData to 7 Oct 2026

Safe Inverse Reinforcement Learning via Control Barrier Function

arXiv 2212.02753 ↗First posted 6 Dec 2022cs.RO cs.AI cs.LGcited by 3

On the map: Offline RL and imitation learning from data → Safe offline RL

Abstract (arXiv)

Learning from Demonstration (LfD) is a powerful method for enabling robots to perform novel tasks as it is often more tractable for a non-roboticist end-user to demonstrate the desired skill and for the robot to efficiently learn from the associated data than for a human to engineer a reward function for the robot to learn the skill via reinforcement learning (RL). Safety issues arise in modern LfD techniques, e.g., Inverse Reinforcement Learning (IRL), just as they do for RL; yet, safe learning in LfD has received little attention. In the context of agile robots, safety is especially vital due to the possibility of robot-environment collision, robot-human collision, and damage to the robot. In this paper, we propose a safe IRL framework, CBFIRL, that leverages the Control Barrier Function (CBF) to enhance the safety of the IRL policy. The core idea of CBFIRL is to combine a loss function inspired by CBF requirements with the objective in an IRL method, both of which are jointly optimized via gradient descent. In the experiments, we show our framework performs safer compared to IRL methods without CBF, that is ~15% and ~20% improvement for two levels of difficulty of a 2D racecar domain and ~ 50% improvement for a 3D drone domain.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 23 of its 36 references that are in the map. First posted on arXiv 6 Dec 2022.

Builds on

23 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Learning Control Barrier Functions from Expert Demonstrations7 Apr

Other references · 22

  1. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  2. 2017Safe Reinforcement Learning via Shieldingcited by 427
  3. 2019Control Barrier Functions: Theory and Applicationscited by 332
  4. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 229
  5. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 159
  6. 2018DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skillscited by 153
  7. 2020Learning from Suboptimal Demonstration via Self-Supervised Reward Regressioncited by 84
  8. 2020Safe Imitation Learning via Fast Bayesian Reward Inference from Preferencescited by 73
  9. 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 71
  10. 2020Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functionscited by 61
  11. 2018Deep Reinforcement Learning for Autonomous Drivingcited by 36
  12. 2021Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violationscited by 36
10 more
  1. 2020Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approachcited by 35
  2. 2021Model-based Constrained Reinforcement Learning using Generalized Control Barrier Functioncited by 30
  3. 2019Deep Bayesian Reward Learning from Preferencescited by 28
  4. 2020Learning Hybrid Control Barrier Functions from Datacited by 24
  5. 2017Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learningcited by 21
  6. 2018Cooperative and Distributed Reinforcement Learning of Drones for Field Coveragecited by 20
  7. 2020Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillationcited by 19
  8. 2017Autonomous Braking System via Deep Reinforcement Learningcited by 18
  9. 2017Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learningcited by 17
  10. 2017Feature Analysis and Selection for Training an End-to-End Autonomous Vehicle Controller Using the Deep Learning Approachcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learningcited by 5
  2. 2025InvThink: Premortem Reasoning for Safer Language Modelscited by 0
  3. 2026Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Learning from Demonstration with Failure Awareness for Safe Robot Navigation
  2. 2023Learning Shared Safety Constraints from Multi-task Demonstrations
  3. 2019Risk-Aware Active Inverse Reinforcement Learning
  4. 2025Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
  5. 2025Towards Safe Robot Foundation Models Using Inductive Biases
  6. 2023Learning Safety Constraints from Demonstrations with Unknown Rewards
  7. 2022Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning
  8. 2022End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions