AUTHOR=Katz Garrett E. , Akshay , Davis Gregory P. , Gentili Rodolphe J. , Reggia James A. TITLE=Tunable Neural Encoding of a Symbolic Robotic Manipulation Algorithm JOURNAL=Frontiers in Neurorobotics VOLUME=15 YEAR=2021 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2021.744031 DOI=10.3389/fnbot.2021.744031 ISSN=1662-5218 ABSTRACT=

We present a neurocomputational controller for robotic manipulation based on the recently developed “neural virtual machine” (NVM). The NVM is a purely neural recurrent architecture that emulates a Turing-complete, purely symbolic virtual machine. We program the NVM with a symbolic algorithm that solves blocks-world restacking problems, and execute it in a robotic simulation environment. Our results show that the NVM-based controller can faithfully replicate the execution traces and performance levels of a traditional non-neural program executing the same restacking procedure. Moreover, after programming the NVM, the neurocomputational encodings of symbolic block stacking knowledge can be fine-tuned to further improve performance, by applying reinforcement learning to the underlying neural architecture.