Sim-to-real transfer in deep reinforcement learning for robotics: a survey

Analysis

Why This Paper Matters

Sim-to-real transfer is a critical challenge in deploying deep reinforcement learning (RL) policies trained in simulation to real-world robots. The gap between simulated and real environments often leads to performance degradation due to modeling inaccuracies, sensor noise, and unmodeled dynamics. This survey, published in 2020, provides a structured overview of the techniques developed to address this issue, making it a valuable resource for both newcomers and experienced researchers.

The paper's focus on end-to-end approaches reflects the dominant paradigm in deep RL for robotics, where policies map raw sensor inputs directly to control actions. By consolidating knowledge on domain randomization, system identification, and other transfer methods, the survey helps practitioners understand the trade-offs between different strategies and choose appropriate methods for their specific applications.

Technical Contributions

The survey categorizes sim-to-real transfer techniques into several key areas:

• Domain Randomization: Varying simulation parameters (e.g., mass, friction, lighting) during training to make policies robust to real-world variability.
• System Identification: Calibrating simulation parameters to match the real system as closely as possible.
• Domain Adaptation: Using techniques like image translation (e.g., CycleGAN) to align simulated and real visual inputs.
• Progressive Networks and Fine-Tuning: Transferring knowledge from simulation to real by fine-tuning or using auxiliary networks.
• Meta-Learning: Training policies that can quickly adapt to new environments with minimal real-world data.

The paper also discusses the importance of using realistic sensor noise and dynamics models, and highlights the role of simulators in enabling safe and scalable training.

Results

As a survey, the paper does not present new experimental results. Instead, it synthesizes findings from numerous prior studies, noting that domain randomization has been particularly successful in tasks like robotic manipulation and locomotion. For example, it references work where policies trained with domain randomization achieved zero-shot transfer to real robots, though with some performance degradation compared to simulation. The survey also notes that system identification can improve transfer but often requires significant engineering effort.

Significance

This survey is significant because it provides a clear taxonomy of sim-to-real transfer methods, which helps researchers understand the landscape and identify gaps. It also underscores the importance of sim-to-real transfer for the broader adoption of deep RL in real-world robotics, where safety and cost constraints make simulation-based training essential. The paper's insights have influenced subsequent research, encouraging the development of more robust and generalizable transfer techniques. For AI practitioners, this survey serves as a practical guide to selecting and implementing sim-to-real methods, ultimately accelerating the deployment of RL-based robotic systems.