On the Stability and Behavioral Diversity of Single and Collective Bernoulli Balls.

The ability to express diverse behaviors is a key requirement for most biological systems. Underpinning behavioral diversity in the natural world is the embodied interaction between the brain, body, and environment. Dynamical systems form the basis of embodied agents, and can express complex behavioral modalities without any conventional computation. While significant study has focused on designing dynamical systems agents with complex behaviors, for example, passive walking, there is still a limited understanding about how to drive diversity in the behavior of such systems. In this article, we present a novel hardware platform for studying the emergence of individual and collective behavioral diversity in a dynamical system. The platform is based on the so-called Bernoulli ball, an elegant fluid dynamics phenomenon in which spherical objects self-stabilize and hover in an airflow. We demonstrate how behavioral diversity can be induced in the case of a single hovering ball via modulation of the environment. We then show how more diverse behaviors are triggered by having multiple hovering balls in the same airflow. We discuss this in the context of embodied intelligence and open-ended evolution, suggesting that the system exhibits a rudimentary form of evolutionary dynamics in which balls compete for favorable regions of the environment and exhibit intrinsic "alive" and "dead" states based on their positions in or outside of the airflow.

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review.

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.

Morphological Evolution of Physical Robots through Model-Free Phenotype Development.

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.

Acute effects of whole-body vibration on trunk and neck muscle activity in consideration of different vibration loads.

The intention of this study was to systematically analyze the impact of biomechanical parameters in terms of different peak-to-peak displacements and knee angles on trunk and neck muscle activity during whole-body vibration (WBV). 28 healthy men and women (age 23 ± 3 years) performed four static squat positions (2 peak-to-peak displacements x 2 knee angles) on a side alternating vibration platform with and without vibration stimulus. Surface electromyography (EMG) was used to record the neuromuscular activity of the erector spinae muscle, the rectus abdominis muscle, and of the splenius muscle. EMG levels normalized to maximal voluntary contractions ranged between 3.2 - 27.2 % MVC during WBV. The increase in muscle activity caused by WBV was significant, particularly for the back muscles, which was up to 19.0 % MVC. The impact of the factor 'condition' (F-values ranged from 13.4 to 132.0, p ≤ 0.001) and of the factor 'peak-to-peak displacement' (F-values ranged from 6.4 to 69.0 and p-values from < 0.001 to 0.01) were statistically significant for each muscle tested. However, the factor 'knee angle' only affected the back muscles (F-value 10.3 and 7.3, p ≤ 0.01). The results of this study should give more information for developing effective and safe training protocols for WBV treatment of the upper body. Key pointsThe maximum levels of muscle activity were significantly reached at high amplitudes at a vibration frequency of 30 Hz.WBV leads to a higher muscle activation of the lower back muscles than of the abdominal muscles.Both knee angles of 30° and 45° have similar effects on the vibration load and represent safe positions to prevent any actual harm.Certain combinations of the biomechanical variables have similar effects on the level of muscle activity.