Controller gains affect learnability differently for behavior cloning, RL from scratch, and sim-to-real transfer, so optimal gains depend on the learning paradigm rather than desired task behavior.
Robot learning from randomized simulations: A review
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A benchmark study finds that increased randomization improves Sim2Real transfer in robotic RL despite trade-offs in simulation learning, with full randomization and fine-tuning outperforming other approaches on the real robot.
citing papers explorer
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Tune to Learn: How Controller Gains Shape Robot Policy Learning
Controller gains affect learnability differently for behavior cloning, RL from scratch, and sim-to-real transfer, so optimal gains depend on the learning paradigm rather than desired task behavior.
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Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
A benchmark study finds that increased randomization improves Sim2Real transfer in robotic RL despite trade-offs in simulation learning, with full randomization and fine-tuning outperforming other approaches on the real robot.