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Sim2Real (Simulation to Reality) transfer addresses the challenge of deploying policies trained in simulation to real-world robots. The domain gap between simulated and physical environments requires specialized techniques.

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Key Topics

  • Domain Randomization: Training with varied simulation parameters
  • Domain Adaptation: Aligning feature distributions across domains
  • System Identification: Calibrating simulation to match reality
  • Reality Gap: Understanding and mitigating simulation-reality differences
Key references: (Bousmalis et al., 2017; Marco et al., 2017; Weber et al., 2017; Pan et al., 2017; Hester et al., 2017)

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References

  • Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., et al. (2017). Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping.
  • Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., et al. (2017). Deep Q-learning from Demonstrations.
  • Marco, A., Berkenkamp, F., Hennig, P., Schoellig, A., Krause, A., et al. (2017). Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization.
  • Pan, X., You, Y., Wang, Z., Lu, C. (2017). Virtual to Real Reinforcement Learning for Autonomous Driving.
  • Weber, T., Racanière, S., Reichert, D., Buesing, L., Guez, A., et al. (2017). Imagination-Augmented Agents for Deep Reinforcement Learning.
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