mlsanityData to 14 Oct 2026

Robust Behavior Cloning Via Global Lipschitz Regularization

arXiv 2506.19250 ↗First posted 24 Jun 2025cs.LG cs.AIcited by 2

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Behavior Cloning (BC) is an effective imitation learning technique and has even been adopted in some safety-critical domains such as autonomous vehicles. BC trains a policy to mimic the behavior of an expert by using a dataset composed of only state-action pairs demonstrated by the expert, without any additional interaction with the environment. However, During deployment, the policy observations may contain measurement errors or adversarial disturbances. Since the observations may deviate from the true states, they can mislead the agent into making sub-optimal actions. In this work, we use a global Lipschitz regularization approach to enhance the robustness of the learned policy network. We then show that the resulting global Lipschitz property provides a robustness certificate to the policy with respect to different bounded norm perturbations. Then, we propose a way to construct a Lipschitz neural network that ensures the policy robustness. We empirically validate our theory across various environments in Gymnasium. Keywords: Robust Reinforcement Learning; Behavior Cloning; Lipschitz Neural Network

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 13 of its 26 references that are in the map. First posted on arXiv 24 Jun 2025.

Builds on

13 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Tight Performance Guarantees of Imitator Policies with Continuous Actions7 Dec

Other references · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,517
  4. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 523
  5. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 346
  6. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 238
  7. 2022Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspectivecited by 51
  8. 2021Policy Smoothing for Provably Robust Reinforcement Learningcited by 40
  9. 2023Improve Robustness of Reinforcement Learning against Observation Perturbations via l_∞ Lipschitz Policy Networkscited by 7
  10. 2016A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitationcited by 6
  11. 2022Certifying Model Accuracy under Distribution Shiftscited by 5
  12. 2021Adversarially Regularized Policy Learning Guided by Trajectory Optimizationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025Balance Equation-based Distributionally Robust Offline Imitation Learningcited by 1
  2. 2026When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisitedcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021On the Benefits of Inducing Local Lipschitzness for Robust Generative Adversarial Imitation Learning
  2. 2024On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
  3. 2024Stable-BC: Controlling Covariate Shift with Stable Behavior Cloning
  4. 2020Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning
  5. 2026Understanding Behavior Cloning with Action Quantization
  6. 2022Robust Imitation of a Few Demonstrations with a Backwards Model
  7. 2026Proximal Action Replacement for Behavior Cloning Actor-Critic in Offline Reinforcement Learning
  8. 2025RISE: Robust Imitation through Stochastic Encoding