Sim-to-Real Transfer for Robots is Hard

Integrate detailed physics models with generative AI techniques to improve the accuracy of simulations and facilitate effective transfer of robotic behavior from simulation to reality.

Robot foundational models can address major obstacles in robot learning and enable training on action-free data including video. This is essential for enabling reasoning about novel situations and robustly handling real-world variability.

Systematically collect and curate large training datasets focusing on tactile interactions to enhance simulation accuracy and real-world performance.