Robotic Manipulation and Locomotion

Principal Investigator(s): 
Stella Yu

Existing methods for robotic manipulation and locomotion overlook real world constraints such as data availability, data efficiency, and data quality.  We explore novel approaches that incorporate curriculum learning, latent space information extraction, and invariant states to improve the generalizability of learned control policies against environmental and robot configurational changes.