
Anchor Critics, a novel method leveraging dual Q-values from both simulated and real-world data, effectively mitigates catastrophic forgetting in reinforcement learning, enabling robust sim-to-real transfer for robotic control tasks, particularly demonstrated in quadrotor flight control.


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Anchor Critics for Robust Sim-to-Real Transfer in Reinforcement Learning


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The authors investigate policy-learning approaches for sim-to-real transfer in robotic manipulation using TIAGo and Nvidia simulators, emphasizing collision-less movement in both simulation and real environments.


exploring-sim-to-real-transfer-in-robotic-manipulation-with-tiago-and-nvidia-simulators


Exploring Sim-to-Real Transfer in Robotic Manipulation with TIAGo and Nvidia Simulators
