Artificial evolution of physical systems is a stochastic optimization method in which physical machines are iteratively adapted to a target function. The key for a meaningful design optimization is the capability to build variations of physical machines through the course of the evolutionary process. The optimization in turn no longer relies on complex physics models that are prone to the reality gap, a mismatch between simulated and real-world behavior. We report model-free development and evaluation of phenotypes in the artificial evolution of physical systems, in which a mother robot autonomously designs and assembles locomotion agents. The locomotion agents are automatically placed in the testing environment and their locomotion behavior is analyzed in the real world. This feedback is used for the design of the next iteration. Through experiments with a total of 500 autonomously built locomotion agents, this article shows diversification of morphology and behavior of physical robots for the improvement of functionality with limited resources.

Abstract-In robotics, controlling the stiffness of the joints that contribute to the robots' degree of freedom dictates the adaptability, versatility, and safety of the whole system. We can achieve variable stiffness or impedance in a robotic system purely by the control or by introducing new material or mechanisms to address cases that require innate safety through system compliancy. This paper presents JammJoint, a compliant and flexible wearable robot, which uses jamming of granular media to vary its stiffness. It consists of a silicone sleeve with hollow sections that are filled with cubic rubber granules and subjected to different levels of vacuum pressure. Unlike contemporary vacuum-based actuators or systems, JammJoint is wearable, portable, and autonomous: It uses a powerful miniature vacuum pump, a small battery, and bluetoothenabled electronics. Experiments revolving around bending and torsional stiffness show that the system is able to achieve up to a fourfold increase in spring stiffness. Further measurements of individual variable stiffness structures indicate that for other modes of deformation, including simply supported bending or compression for alternative linear applications, higher changes in stiffness over a factor of seven are possible. These aspects make mobile jammingbased stiffness variation as wearable joint assistance promising for future applications such as rehabilitation after injuries and joint support in challenging working conditions. Index Terms-Wearable robots, soft material robotics, variable stiffness joint, vacuum jamming, hydraulic/pneumatic actuators.

This work presents a series of demonstrations of our self-reconfigurable modular robots (SRMR) "Roombots" in the context of adaptive and assistive furniture. In literature, simulations are often ahead of what currently can be demonstrated in hardware with such systems due to significant challenges in transferring them to the real world. Here, we describe how Roombots tackled these difficulties in real hardware and focus qualitatively on selected hardware experiments rather than on quantitative measurements (in hardware and simulation) to showcase the many possibilities of an SRMR. We envision Roombots to be used in our living space and define five key tasks that such a system must possess. Consequently, we demonstrate these tasks, including self-reconfiguration with 12 modules (36 Degrees of Freedom), autonomously moving furniture, object manipulation and gripping capabilities, human-module-interaction and the development of an easy-to-use user interface. We conclude with the remaining challenges and point out possible directions of research for the future of adaptive and assistive furniture with Roombots.

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

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