Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics (2503.10949v1)

Abstract: Domain randomization has emerged as a fundamental technique in reinforcement learning (RL) to facilitate the transfer of policies from simulation to real-world robotic applications. Many existing domain randomization approaches have been proposed to improve robustness and sim2real transfer. These approaches rely on wide randomization ranges to compensate for the unknown actual system parameters, leading to robust but inefficient real-world policies. In addition, the policies pretrained in the domain-randomized simulation are fixed after deployment due to the inherent instability of the optimization processes based on RL and the necessity of sampling exploitative but potentially unsafe actions on the real system. This limits the adaptability of the deployed policy to the inevitably changing system parameters or environment dynamics over time. We leverage safe RL and continual learning under domain-randomized simulation to address these limitations and enable safe deployment-time policy adaptation in real-world robot control. The experiments show that our method enables the policy to adapt and fit to the current domain distribution and environment dynamics of the real system while minimizing safety risks and avoiding issues like catastrophic forgetting of the general policy found in randomized simulation during the pretraining phase. Videos and supplementary material are available at https://safe-cda.github.io/.

Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics

The paper "Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics" presents an intricate framework for enhancing the deployment of Reinforcement Learning (RL) policies in robotic systems through an approach termed Safe Continual Domain Adaptation (SCDA). The SCDA framework addresses the robustness and adaptability challenges that arise when transitioning RL policies trained in simulation environments to real-world applications. This transition, commonly referred to as sim2real transfer, has been impeded by the reality gap—differences between simulated models and their real-world counterparts.

Methodology Overview

The authors have identified that existing domain randomization techniques, although effective in improving policy robustness, do not always yield efficient policies once deployed in the real world. Moreover, these policies, once deployed, are usually fixed and unable to adapt to variations in system dynamics or environmental changes. The proposed SCDA framework integrates the techniques from safe RL and continual learning (CL), aiming to facilitate safe and adaptive policy adjustments in the real-world robotic control systems.

SCDA's methodology is detailed through two distinct stages:

1. Pretraining Stage:
   • Here, the RL agent is pre-trained in a domain-randomized simulator. This simulation employs wide randomization ranges to account for variability and uncertainty in real-world system parameters. Randomization exposes the agent to a wide range of scenarios, forcing it to learn a robust policy. Moreover, the pretraining involves the Policy Constrained Reinforcement Learning (PCRPO) algorithm to optimize for safety alongside task performance, ensuring that the learned policy adheres to defined safety constraints even in exploratory phases.
2. Adaptation Stage:
   • Post-deployment, the SCDA enables policies to adjust to the actual system dynamics using continual learning strategies, specifically Elastic Weight Consolidation (EWC). EWC allows for adaptation without overwriting crucial pre-trained knowledge, avoiding catastrophic forgetting—a prevalent issue in traditional learning paradigms. The continual learning component emphasizes retaining important knowledge from pretraining while allowing sufficient flexibility to latch onto changes observed in the real environment.

Empirical Evaluation and Results

The paper's empirical evaluation is divided into two experiments: a simplified reach-and-balance task and a more complex object grasping task, both involving a KUKA robotic manipulator. The comparison across different strategies (e.g., zero-shot transfer, domain adaptation with and without safety, and SCDA) demonstrated SCDA's superior capability in maintaining safety constraints while adapting policies over changing target positions.

Key takeaways from the experiments include:

• SCDA significantly outperforms zero-shot transfer and other adaptation strategies in terms of maintaining the balance between task performance and safety constraints.
• The incorporation of safety in policy optimization during adaptation proves critical, as observed with the success of SCDA compared to non-safety-adaptive approaches.
• The use of Fisher Information Matrix in regularizing policy parameter updates under SCDA ensures that adaptation focuses on non-critical parameters while protecting critical ones learned during pretraining.

Implications and Future Directions

The implications of introducing SCDA are profound in robotics, where adaptability to real-world dynamics is essential, especially in environments subject to frequent and unpredictable changes. SCDA provides a pathway for deploying RL policies that are not only robust but can also continuously self-optimize in response to evolving operational conditions.

The paper also sets the stage for future work in autonomous robotics, suggesting integration with control-based methods to further enhance safety and address the challenges of unsupervised domain shifts. Moreover, addressing the out-of-distribution detection to signal when a domain shift occurs could significantly augment SCDA's adaptability ceiling.

By merging safe RL with continual learning in a domain adaptation context, this research contributes a methodologically sound and practical framework poised to bolster the capabilities of robotic applications across various industrial and operational scenarios. The authors make a cautious yet impactful proposition that aligns with a broader objective in reinforcement learning—creating systems that securely, effectively, and independently adapt in real-world environments.