Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators
Michael Przystupa,Kerrick Johnstonbaugh,Zichen Zhang,Laura Petrich,Masood Dehghan,Faezeh Haghverd,Martin Jagersand,Michael Przystupa,Kerrick Johnstonbaugh,Zichen Zhang,Laura Petrich,Masood Dehghan,Faezeh Haghverd,Martin Jagersand
Identifying an appropriate task space can simplify solving robotic manipulation problems. One solution is deploying control algorithms in a learned low-dimensional action space. Linear and nonlinear action mapping methods have trade-offs between simplicity and the ability to express motor commands outside of a single low-dimensional subspace. We propose that learning local linear action representa...
