Transfer from simulation to real world through learning deep inverse dynamics model

Researchers propose a sim-to-real method that turns a policy’s intended next state into real robot actions using a learned deep inverse dynamics model.

An incremental data collection scheme helps improve the inverse model over time with real-world experience.

The approach is designed to bridge simulation-to-hardware gaps in friction, contact, mass, and geometry.

Experiments show stronger transfer performance than several baseline methods.