Continuous learning of emergent behavior in robotic matter.

One of the main challenges in robotics is the development of systems that can adapt to their environment and achieve autonomous behavior. Current approaches typically aim to achieve this by increasing the complexity of the centralized controller by, e.g., direct modeling of their behavior, or implementing machine learning. In contrast, we simplify the controller using a decentralized and modular approach, with the aim of finding specific requirements needed for a robust and scalable learning strategy in robots. To achieve this, we conducted experiments and simulations on a specific robotic platform assembled from identical autonomous units that continuously sense their environment and react to it. By letting each unit adapt its behavior independently using a basic Monte Carlo scheme, the assembled system is able to learn and maintain optimal behavior in a dynamic environment as long as its memory is representative of the current environment, even when incurring damage. We show that the physical connection between the units is enough to achieve learning, and no additional communication or centralized information is required. As a result, such a distributed learning approach can be easily scaled to larger assemblies, blurring the boundaries between materials and robots, paving the way for a new class of modular "robotic matter" that can autonomously learn to thrive in dynamic or unfamiliar situations, for example, encountered by soft robots or self-assembled (micro)robots in various environments spanning from the medical realm to space explorations.

Suspended heated silicon platform for rapid thermal control of surface reactions with application to carbon nanotube synthesis.

Rapid continuous thermal control of chemical reactions such as those for chemical vapor deposition (CVD) growth of nanotubes and nanowires cannot be studied using traditional reactors such as tube furnaces, which have large thermal masses. We present the design, modeling, and verification of a simple, low-cost reactor based on resistive heating of a suspended silicon platform. This system achieves slew rates exceeding 100 degrees C/s, enabling studies of rapid heating and thermal cycling. Moreover, the reaction surface is available for optical monitoring. A first-generation CVD apparatus encapsulates the heated silicon platform inside a sealed quartz tube, and initial experiments demonstrate growth of films of tangled single-wall and aligned multiwall carbon nanotubes using this system. The reactor can be straightforwardly scaled to larger or smaller substrate sizes and may be extended for a wide variety of reactions, for performing in situ reaction diagnostics, for chip-scale growth of nanostructures, and for rapid thermal processing of microelectronic and micromechanical devices.