Sim To Real Transfer Through Domain Randomization And Robust Policy Training

via “domain randomization for sim-to-real transfer”

* ⭐ 10/2022: [Discovering faster matrix multiplication algorithms with reinforcement learning (AlphaTensor)](https://www.nature.com/articles/s41586-022%20-05172-4)

Unique: Applies curriculum-style domain randomization across thousands of parallel environments, sampling new randomization parameters per episode to create an implicit ensemble of physics models that the policy must simultaneously adapt to

vs others: Achieves real-world transfer without manual tuning by training against a distribution of simulated physics, compared to single-model simulation training that typically requires extensive real-world fine-tuning

via “sim-to-real transfer through domain randomization and robust policy training”

* ⭐ 02/2022: [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://proceedings.mlr.press/v164/jang22a.html)

Unique: Combines domain randomization in simulation with targeted fine-tuning on real-world data, using robust training objectives that prevent catastrophic forgetting of simulation-learned features while adapting to real-world dynamics. The approach treats simulation and real-world data as complementary rather than competing sources.

vs others: More sample-efficient than pure real-world training by leveraging simulation pre-training, and more practical than pure simulation approaches by fine-tuning on real data to handle the reality gap. Outperforms naive sim-to-real transfer by using domain randomization to improve generalization.

via “sim-to-real transfer and domain randomization for robot learning”

## Historical Papers <a name="history"></a>

Unique: Combines simulation-based pre-training with domain randomization and real-world fine-tuning to improve sample efficiency and generalization. This approach leverages the abundance of simulation data to reduce real-world data requirements while maintaining performance on real hardware.

vs others: Reduces real-world data collection costs compared to learning from scratch on real robots, while maintaining or improving generalization through domain randomization and transfer learning.