RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real (2006.09001v1)

Abstract: Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without the need for manually engineering or prior learning a perception system. However, data for RL is collected via running an agent in the desired environment, and for applications like robotics, running a robot in the real world may be extremely costly and time consuming. Simulated training offers an appealing alternative, but ensuring that policies trained in simulation can transfer effectively into the real world requires additional machinery. Simulations may not match reality, and typically bridging the simulation-to-reality gap requires domain knowledge and task-specific engineering. We can automate this process by employing generative models to translate simulated images into realistic ones. However, this sort of translation is typically task-agnostic, in that the translated images may not preserve all features that are relevant to the task. In this paper, we introduce the RL-scene consistency loss for image translation, which ensures that the translation operation is invariant with respect to the Q-values associated with the image. This allows us to learn a task-aware translation. Incorporating this loss into unsupervised domain translation, we obtain RL-CycleGAN, a new approach for simulation-to-real-world transfer for reinforcement learning. In evaluations of RL-CycleGAN on two vision-based robotics grasping tasks, we show that RL-CycleGAN offers a substantial improvement over a number of prior methods for sim-to-real transfer, attaining excellent real-world performance with only a modest number of real-world observations.

Insights into RL-CycleGAN: A Reinforcement Learning Aware Simulation-To-Real Approach

The paper introduces a novel approach known as RL-CycleGAN, which effectively addresses the challenges of transferring reinforcement learning (RL) policies trained in simulation to real-world systems. The approach strategically combines Cycle-consistent Generative Adversarial Networks (CycleGANs) with RL to preserve task-specific features during the simulation-to-real transfer of policies. This method holds significant promise for vision-based reinforcement learning, where policies often suffer from the visual discrepancies between simulated and real environments.

Approach

The RL-CycleGAN approach integrates a reinforcement learning-driven loss, termed RL-scene consistency loss, into the CycleGAN framework to address limitations in task performance when applying visually adapted images for RL tasks. This loss ensures that the translation from simulated to real-world images does not alter the Q-values associated with critical task-specific features, such as object and robot locations pertinent to grasping tasks. This strategy circumvents the need for excessive manual engineering of domain adaptation features, an aspect where other solutions often fall short when applied to complex real-world tasks.

Evaluation and Results

The effectiveness of RL-CycleGAN was demonstrated through rigorous evaluations on two distinct robotic grasping systems. The evaluation results are quite compelling, indicating substantial improvements over previous techniques. In particular, RL-CycleGAN achieved 70% grasping success from purely simulated data on a KUKA IIWA robot, which represents a notable enhancement over methods like standard GAN and GraspGAN, which reached only 29% and 63% success, respectively.

Additionally, when RL-CycleGAN was used in conjunction with real-world data, it demonstrated the potential to drastically reduce the amount of real-world data required. For instance, with a mere 5,000 real-world trials, RL-CycleGAN was able to achieve a grasp success rate of 75%, whereas traditional methods typically required many more samples to reach similar performance levels. These results illustrate not only the robustness and scalability of the method but also its significant contribution to reducing the dependency on extensive real-world data collection, a common bottleneck in RL-based robotic applications.

Implications and Future Directions

The paper presents strong numerical evidence for the capabilities of RL-CycleGAN in enhancing transfer learning in RL by introducing a consistent, non-task-specific method of adapting images for robotic tasks. This likely has numerous practical implications, including reduced time and cost associated with deploying RL models in real-world settings, as well as the ability to train models on a broader array of tasks without bespoke adaptations for each task.

Theoretically, this approach prompts a re-evaluation of how generative models can be efficiently paired with RL tasks by leveraging task-specific losses. Moving forward, potential areas of improvement could involve extending the RL-CycleGAN to handle physics-based discrepancies between simulated and real environments or exploring stochastic GAN outputs for a richer and potentially more robust policy learning.

In essence, RL-CycleGAN presents a substantial stride forward in overcoming sim-to-real challenges inherent in reinforcement learning, offering a promising framework for future exploration and development in applied AI settings.