20241122 Sgibo

Our new preprint on Simulation-Aided Policy Tuning for Black-Box Robot Learning is finally online! This research tackles the challenge of data-efficient fine-tuning of robot behaviors. Building on our prior work, we propose a local Bayesian optimization algorithm that leverages both robot experiments and simulation to speed up learning. Check out the preprint and the video for more details!