Robot learning: closing the reality gap!

This program aims to close the gap between simulated and real-world robotic systems by advancing principled approaches to robot learning and control. Its research pillars include robot motion and action generation, learning-based interaction, sensorimotor adaptation, sim-to-real transfer, and machine learning methods to improve robustness and real-world performance.