System identification for sim-to-real
System identification for sim-to-real involves creating models that bridge the gap between simulated environments and real-world applications, enhancing the transferability of learned behaviors.
sim-to-real
Sim-to-real transfer for RL
Sim-to-real transfer for reinforcement learning (RL) involves adapting models trained in simulation to perform in real-world environments, addressing challenges like domain gaps.