Complex systems are characterized by many independent components whose low-level actions produce collective high-level results. Predicting high-level results given low-level rules is a key open challenge; the inverse problem, finding low-level rules that give specific outcomes, is in general still less understood. We present a multi-agent construction system inspired by mound-building termites, solving such an inverse problem. A user specifies a desired structure, and the system automatically generates low-level rules for independent climbing robots that guarantee production of that structure. Robots use only local sensing and coordinate their activity via the shared environment. We demonstrate the approach via a physical realization with three autonomous climbing robots limited to onboard sensing. This work advances the aim of engineering complex systems that achieve specific human-designed goals.

Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are frequently used to overcome these difficulties. We derive quantitative learning curves for three online training methods used with a linear perceptron: direct gradient descent, node perturbation, and weight perturbation. The maximum learning rate for the stochastic methods scales inversely with the first power of the dimensionality of the noise injected into the system; with sufficiently small learning rate, all three methods give identical learning curves. These results suggest guidelines for when these stochastic methods will be limited in their utility, and considerations for architectures in which they will be effective.

Abstract-Collective construction is the research area in which autonomous multi-robot systems build structures according to user specifications. Here we present a hardware system and high-level control scheme for autonomous construction of 3D structures under conditions of gravity. The hardware comprises a mobile robot and specialized passive blocks; the robot is able to manipulate blocks to build desired structures, and can maneuver on these structures as well as in unstructured environments. We describe and evaluate the robot's key capabilities of climbing, navigation, and manipulation, and demonstrate its ability to perform complex tasks that combine these capabilities by having it autonomously build a ten-block staircase taller than itself. In addition, we outline a simple decentralized control algorithm by which multiple simultaneously active robots could autonomously build user-specified structures, working from a high-level description as input.

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