Felch A, Granger R (2011). Sensor-rich robots driven by real-time brain circuit algorithms. Neuromorphic and Brain-Based Robots, 58-84.

Felch A, Granger R

Abstract

The analysis of particular telencephalic systems has led to derivation of algorithmic statements of their operation, which have grown to include communicating systems from sensory to motor and back. Like the brain circuits from which they are derived, these algorithms (e.g. Granger, 2006) perform and learn from experience. Their per- ception and action capabilities are often initially tested in simulated environments, which are more controllable and repeatable than robot tests, but it is widely recognized that even the most carefully devised simulated environments typically fail to transfer well to real-world settings.

Robot testing raises the specter of engineering requirements and programming minutiae, as well as sheer cost, and lack of standardization of robot platforms. For brain-derived learning systems, the primary desideratum of a robot is not that it have advanced pinpoint motor control, nor extensive scripted or preprogrammed behaviors. Rather, if the goal is to study how the robot can acquire new knowledge via actions, sensing results of actions, and incremental learning over time, as children do, then rela- tively simple motor capabilities will suffice when combined with high-acuity sensors (sight, sound, touch) and powerful onboard processors.

The Brainbot platform is an open-source, sensor-rich robot, designed to enable testing of brain-derived perceptual, motor, and learning algorithms in real-world set- tings. The system is intended to provide an inexpensive yet highly trainable vehicle to broaden the availability of interactive robots for research. The platform is capable of only relatively simple motor tasks, but contains extensive sensors (visual, auditory, tactile), intended to correspond to crucial basic enabling characteristics for long-term real-world learning. Humans (and animals) missing sensors and limbs can nonetheless function exceedingly well in the world as long as they have intact brains; analogously, Brainbot has reasonable, limited motor function and all necessary sensors to enable it to function at a highly adaptive level: that is, prioritizing sensorimotor learning over unnecessarily complex dexterity.