Structurally programmed capillary folding of carbon nanotube assemblies.

We demonstrate the fabrication of horizontally aligned carbon nanotube (HA-CNT) networks by spatially programmable folding, which is induced by self-directed liquid infiltration of vertical CNTs. Folding is caused by a capillary buckling instability and is predicted by the elastocapillary buckling height, which scales with the wall thickness as t(3/2). The folding direction is controlled by incorporating folding initiators at the ends of the CNT walls, and the initiators cause a tilt during densification which precedes buckling. By patterning these initiators and specifying the wall geometry, we control the dimensions of HA-CNT patches over 2 orders of magnitude and realize multilayered and multidirectional assemblies. Multidirectional HA-CNT patterns are building blocks for custom design of nanotextured surfaces and flexible circuits.

Behavior learning of a face robot based on the characteristics of human instruction

Computer simulation experiments were performed to examine the effectiveness of OR- and comparative-reinforcement learning algorithms. In the simulation, human rewards were given as +1 and -1. Two models of human instruction that determine which reward is to be given in every step of a human instruction were used. Results show that human instruction may have a possibility of including both model-A and model-B characteristics, and it can be expected that the comparative-reinforcement learning algorithm is more effective for learning by human instructions.