SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning (2101.06005v2)

Abstract: As learning-based approaches progress towards automating robot controllers design, transferring learned policies to new domains with different dynamics (e.g. sim-to-real transfer) still demands manual effort. This paper introduces SimGAN, a framework to tackle domain adaptation by identifying a hybrid physics simulator to match the simulated trajectories to the ones from the target domain, using a learned discriminative loss to address the limitations associated with manual loss design. Our hybrid simulator combines neural networks and traditional physics simulation to balance expressiveness and generalizability, and alleviates the need for a carefully selected parameter set in System ID. Once the hybrid simulator is identified via adversarial reinforcement learning, it can be used to refine policies for the target domain, without the need to interleave data collection and policy refinement. We show that our approach outperforms multiple strong baselines on six robotic locomotion tasks for domain adaptation.

• The paper proposes SimGAN, a novel framework using a hybrid simulator and adversarial reinforcement learning to identify real-world dynamics for improved sim-to-real transfer.
• SimGAN employs Generative Adversarial Networks (GANs) to learn a discriminative loss, enabling trajectory matching through distribution-level supervision and avoiding manual parameter tuning.
• Experimental results demonstrate SimGAN's superior performance over baseline methods on six robotic locomotion tasks with varying dynamics discrepancies, showing its effectiveness in adapting policies.

An Analysis of the SimGAN Framework for Hybrid Simulator Identification in Domain Adaptation

The paper "SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning" proposes a sophisticated framework designed to mitigate the challenges posed by domain adaptation, particularly in the context of sim-to-real transfer in robotics. This endeavor is pivotal for optimizing learned policies when transitioning from simulated environments to real-world domains with disparate dynamics.

Core Contributions and Methodology

The paper introduces SimGAN, a novel hybrid simulator combining neural networks with traditional physics simulations. The crux of this approach lies in accurately modeling the unmodeled discrepancies between simulated and real-world environments, thus facilitating effective policy transfer without exhaustive parameter selection inherent in traditional System Identification (SysID) methods.

A key element of SimGAN is the adoption of Generative Adversarial Networks (GANs) to contrive a learned discriminative loss. This technique enhances trajectory matching through distribution-level supervision as opposed to heuristic design of loss functions. The adversarial reinforcement learning (RL) paradigm employs GANs to differentiate between source and target domain dynamics, effectively refining the simulator parameters for achieving domain adaptation.

Numerical Results and Performance Evaluation

The paper validates the SimGAN framework through experimentation on six robotic locomotion tasks using two robots: a simulated 2D hopper and the Unitree Laikago quadruped. The results demonstrate SimGAN’s superior performance over multiple robust baseline methods—Fine-tuning, Domain Randomization, DR combined with Fine-tuning, and variants of System ID—across various target environments characterized by distinct dynamics discrepancies including contact, actuator, and inertial differences. Particularly noteworthy is the framework's capacity to seamlessly integrate learned simulator dynamics for multiple motor skills without modification, underscoring its adaptability.

Implications and Future Prospects

In practical terms, SimGAN represents a significant advancement in reducing manual efforts associated with sim-to-real transfer of RL policies. By abolishing the need for paired trajectory comparison and manual design of randomization parameters, the framework contributes to saving resources and improving policy refinement efficiency.

Theoretically, the successful deployment of GANs in conjunction with RL for simulator identification enriches the toolkit available for approaching domain adaptation challenges. This synergy broadens the horizon for model expressiveness while retaining stability within state-action distribution shifts—a salient consideration for real-world robotics applications.

Moving forward, expanding the hybrid simulator’s parameterization to encompass diverse physical aspects while enforcing constraints, such as energy conservation, could yield further improvements. Additionally, empirical validation on physical robots post-COVID-19 restrictions will be crucial to corroborate the framework’s effectiveness beyond simulated domains.

Conclusion

SimGAN stands as a compelling integration of adversarial learning for simulating unmodeled dynamics in domain adaptation tasks, promising substantial improvements in sim-to-real transfer efficacy. Its strategy of leveraging GAN-generated distribution-level insights delineates a progressive shift from traditional methods, paving the way for enhanced robustness and generalizability of robotic policies in varied real-world implementations.