Graphic processing units based brain research helps robot perform tasks

PUNE: Two researchers in Japan have used graphic processing units(GPUs) and the CUDA parallel programming model to create a 100,000 neuron simulation of the human cerebellum, one of the largest simulations of its kind in the world. Interestingly, they have put their model to the test by applying this knowledge to teach a robot to learn to hit a ball.

Tadashi Yamazakiat the University of Electro-Communications in Tokyo, and Jun Igarashi at Okinawa Institute of Science and Technology Graduate University in Okinawa, recently issued a paper detailing how they used NVIDIA GPUs to build a large-scale network model of the human cerebellum.

The two believe that modelling the cerebellum could help robots move around more easily and learn to respond autonomously to their environments, a problem that has proven to be a daunting problem for conventional approaches. And in turn, they hope to shed more light on how cerebellum motor control works.

Their work is part of a subfield of robotics called "biomimetic" robotics that aims to help robots deal with ever-changing environments by mimicking some of the ways that humans solve these problems.

According to Igarashi, their work involved modeling realistic neural brain function to enable the robot to interact with its environment, which is no easy task. "Our physical actions change the environment, which changes the sensory input to human brain. The brain then processes this changed sensory information and determines what action to take. It is called the 'sensorimotor loop'" Igarashi explains. "The brain must continue to choose appropriate actions on the basis of gradually-changing sensory information."

One of the biggest challenges in modeling neural brain function is simulation speed. Using a CPU alone it took 98 seconds of compute time to figure out how to respond to a stimulus lasting just one second. Using GPUs resulted in a 100x speedup, giving the GPU-based system the speed needed to handle real world tasks.

To show their system in action, the researchers demonstrated their robotic system learning - in real time - how to hit a small plastic ball thrown by a toy pitching machine with a round plastic racket.

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