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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Tunable Neural Encoding of a Symbolic Robotic Manipulation Algorithm.

Garrett E Katz¹, Akshay¹, Gregory P Davis²

¹Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, United States.

Frontiers in Neurorobotics

|December 31, 2021

Summary

This summary is machine-generated.

We developed a novel neurocomputational controller using a neural virtual machine (NVM) for robotic manipulation. This AI controller replicates traditional programming performance and can be further optimized with reinforcement learning.

Keywords:

explainable AI neurosymbolic architectures policy optimization reinforcement learning robotic manipulation

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Published on: February 12, 2017

Area of Science:

Robotics
Artificial Intelligence
Neuroscience

Background:

Traditional robotic control relies on symbolic algorithms.
Recent advancements include neural virtual machines (NVMs) as purely neural recurrent architectures.
NVMs emulate Turing-complete symbolic virtual machines.

Purpose of the Study:

To present a neurocomputational controller for robotic manipulation using the NVM.
To evaluate the NVM's ability to execute symbolic algorithms for robotic tasks.
To explore fine-tuning NVMs with reinforcement learning for performance enhancement.

Main Methods:

Developed a neurocomputational controller based on the neural virtual machine (NVM).
Programmed the NVM with a symbolic algorithm for blocks-world restacking problems.
Executed the NVM controller in a robotic simulation environment.
Applied reinforcement learning to fine-tune the NVM's neurocomputational encodings.

Main Results:

The NVM-based controller successfully replicated execution traces and performance of traditional non-neural programs.
The controller demonstrated faithful execution of symbolic algorithms in a robotic simulation.
Fine-tuning the NVM using reinforcement learning led to improved performance.

Conclusions:

The neural virtual machine (NVM) offers a viable neurocomputational approach for robotic manipulation.
NVMs can effectively execute symbolic algorithms, bridging neural and symbolic AI.
Reinforcement learning can further enhance the performance of NVM-based robotic controllers.