Since a humanoid robot takes the morphology of human, users as pilots will intuitively expect that they can freely manipulate the humanoid extremities. However, it is difficult to simultaneously issue such multiple control inputs to the whole body with simple devices. It is useful for motion pattern generation to get mapping functions bidirectionally between a large number of control inputs for a humanoid robot and a small number of control inputs that a user can intentionally operate. For the purpose of generation of voluntary movement of humanoid extremities, we introduce hierarchical NLPCA neural networks that forms low dimensional variables out of multi-variate inputs of joint angles. The problem is to find common space that affords unified manipulable variables not only for specific motion like walk but also multiple whole body motion patterns. The interesting result is shown that 1 dimensional inputs can generate an approximate walking pattern, and also 3 dimensional inputs does 9 types of motion patterns.

For the development of an intelligent robot with many degrees-of-freedom, the reduction of the whole body motion and the implementation of the brain-like information system is necessary. We propose a reduction method of the whole body motion based on singular value decomposition and a design method of the brain-like information processing system using a nonlinear dynamics network with polynomial configuration. By using the proposed method, we design the humanoid whole body motion that is caused by the input sensor signals.

Robotics can serve as a testbed for cognitive theories. One behavioral criterion for comparing theories is the extent to which their implementations can learn to exploit new environmental opportunities. Furthermore, a robotics testbed forces researcher to confront fundamental issues concerning how internal representations are grounded in activity. In our approach, a mobile robot takes the role of a creature that must survive in an unknown environment. The robot has no a priori knowledge about what constitutes a suitable goal-what is edible, inedible, or dangerous-or even its shape or how its body works. Nevertheless, the robot learns how to survive. The robot does this by tracking segmented regions of its camera image while moving. The robot projects these regions into a canonical wavelet domain that highlights color and intensity changes at various scales. This reveals sensory invariance that is readily extracted with Bayesian statistics. The robot simultaneously learns an adaptable sensorimotor mapping by recording how motor signals transform the locations of regions on its camera image. The robot learn about its own physical extension when it touches an object. But it also undergoes an internal state change analogous to the thirst quenching or nausea producing effects of intake in animals. This allows the robot to learn what an object affords by relating these effects to learned clusters of invariance. In this way primitive symbols emerge. These protosymbols provide the robot with goals that it can achieve by using its sensorimotor mapping to navigate, for example, toward food and away from danger.

Robotics can serve as a testbed for cognitive theories. One behavioral criterion for comparing theories is the extent to which their implementations can learn to exploit new environmental opportunities. Furthermore, a robotics testbed forces researchers to confront fundamental issues concerning how internal representations are grounded in activity. In our approach, a mobile robot takes the role of a creature that must survive in an unknown environment. The robot has no a priori knowledge about what constitutes a suitable goal, what is edible, inedible, or dangerous, or even its shape or how its body works. Nevertheless, the robot learns how to survive. The robot does this by tracking segmented regions of its camera image while moving. The robot projects these regions into a canonical wavelet domain that highlights color and intensity changes at various scales. This reveals sensory invariance that is readily extracted with Bayesian statistics. The robot simultaneously learns an adaptable sensorimotor mapping by recording how motor signals transform the locations of regions on its camera image. The robot learns about its own physical extension when it touches an object, but it also undergoes an internal state change analogous to the thirst quenching or nausea producing effects of intake in animals. This allows the robot to learn what an object affords: is it edible or poisonous, by relating these effects to learned clusters of invariance. In this way primitive symbols emerge. These proto-symbols provide the robot with goals that it can achieve by using its sensorimotor mapping to navigate, for example, toward food and away from danger.