Abstract
Neurally inspired robotics already has a long history that includes reactive systems emulating reflexes, neural oscillators to generate movement patterns, and neural networks as trainable filters for high-dimensional sensory information. Neural inspiration has been less successful at the level of cognition. Decision-making, planning, building and using memories, for instance, are more often addressed in terms of computational algorithms than through neural process models. To move neural process models beyond reactive behavior toward cognition, the capacity to autonomously generate sequences of processing steps is critical. We review a potential solution to this problem that is based on strongly recurrent neural networks described as neural dynamic systems. Their stable states perform elementary motor or cognitive functions while coupled to sensory inputs. The state of the neural dynamics transitions to a new motor or cognitive function when a previously stable neural state becomes unstable. Only when a neural robotic system is capable of acting autonomously does it become a useful to a human user. We demonstrate how a neural dynamic architecture that supports autonomous sequence generation can engage in such interaction. A human user presents colored objects to the robot in a particular order, thus defining a serial order of color concepts. The user then exposes the system to a visual scene that contains the colored objects in a new spatial arrangement. The robot autonomously builds a scene representation by sequentially bringing objects into the attentional foreground. Scene memory updates if the scene changes. The robot performs visual search and then reaches for the objects in the instructed serial order. In doing so, the robot generalizes across time and space, is capable of waiting when an element is missing, and updates its action plans online when the scene changes. The entire flow of behavior emerges from a time-continuous neural dynamics without any controlling or supervisory algorithm.
Highlights
Inspired robotics already has a long history
We generate individual goal-directed reaches from an active transient solution of a recurrent neural dynamics. We extend this tradition by providing a neural dynamic architecture that obtains from the visual array a neural representation of the targets of a reaching movement
We demonstrate how a neural dynamic system may autonomously generate the sequence of attentional selections to build a visual scene memory that is intermittently coupled to the visual array, and is sensitive to change and capable of updating in response to such change
Summary
One strand goes back to Grey’s electronic turtle (Grey, 1950) and Braitenberg’s thought experiments on vehicles (Braitenberg, 1984) This line of work reached maturity in behavior-based robotics (Brooks, 1991; Mataric, 1998) in which flexibility emerges from the coordination of elementary behaviors, each establishing a direct link from sensory inputs to actuators, in the manner of reflex loops. This is suited to conceptual “vehicles,” robotic systems in which the sensors are mounted on the moving actuator. We will study how memory for serial order can be built and used to act sequentially in new environments
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