Large-scale automated investigation of free-falling paper shapes via iterative physical experimentation

Settling aerodynamics is a driver of symmetry in deciduous tree leaves

Leaves shed by deciduous trees contain 40% of the annually sequestered carbon and include nutrients vital to the expansion and health of forest ecosystems. To achieve this, leaves must fall quickly to land near the parent tree-otherwise, they are lost to the wind, like pollen or gliding seeds. However, the link between leaf shape and sedimentation speed remains unclear. To gauge the relative performance of extant leaves, we developed an automated sedimentation apparatus capable of performing approximately 100 free-fall experiments per day on biomimetic paper leaves. The majority of 25 representative leaves settle at rates similar to our control (a circular disc). Strikingly, the Arabidopsis mutant asymmetric leaves1 (as1) fell 15% slower than the wild-type. Applying the as1-digital mutation to deciduous tree leaves revealed a similar speed reduction. Data correlating shape and settling across a broad range of natural, mutated and artificial leaves support the fast-leaf hypothesis: deciduous leaves are symmetric and relatively unlobed partly because this maximizes their settling speed and concomitant nutrient retention.

Quantifying spatiotemporal patterns in classical and quantum systems out of equilibrium

A rich variety of nonequilibrium dynamical phenomena and processes unambiguously calls for the development of general numerical techniques to probe and estimate a complex interplay between spatial and temporal degrees of freedom in many-body systems of completely different nature. In this work we provide a solution to this problem by adopting a structural complexity measure to quantify spatiotemporal patterns in the time-dependent digital representation of a system. On the basis of very limited amount of data our approach allows us to distinguish different dynamical regimes and define critical parameters in both classical and quantum systems. By the example of the discrete time crystal realized in nonequilibrium quantum systems we provide a complete low-level characterization of this nontrivial dynamical phase with only processing bitstrings, which can be considered as a valuable alternative to previous studies based on the calculations of qubit correlation functions.

State-transfer modeling collective behavior of multi-ball Bernoulli system based on local interaction forces

Collective behavior observed in nature has been actively employed in swarm robotics. In order to better respond to external cues, the agents in such systems organize themselves in an ordered structure based on simple local rules. The central assumption, in swarm robotics, is that all agents in the system collaborate to fulfill a common goal. In nature, however, many multi-agent systems exhibit a more complex collective behavior involving a certain level of competition. One representative example of complex collective behavior is a multi-ball Bernoulli-ball system. In this paper, by extracting local force among the Bernoulli balls, we approximated the state-transfer model mapping interaction forces to observed behaviors. The results show that the collective Bernoulli-ball system spent 41% of its time on competitive behaviors, in which up to 84% of the interaction state is unorganized. The rest 59% of the time is spent on collaborative behavior. We believe that the novel proposed model opens new avenues in swarm robotics research.

Morphological Sensitivity and Falling Behavior of Paper V-Shapes

Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) system that exhibits a set of discrete falling behaviors across the morphological parameter space. Our previous work has investigated how morphology influences dominant falling behaviors in the VSFP system. In this article we build on this analysis to investigate the nature of behavioral transitions in the same system. First, we investigate stochastic behavior transitions. We demonstrate how morphology influences the likelihood of different transitions, with certain morphologies leading to a wide range of possible paths through the behavior-space. Second, we investigate deterministic transitions. To investigate behaviors over longer time periods than available in falling experiments we introduce a new experimental platform. We demonstrate how we can induce behavior transitions by modulating the energy input to the system. Certain behavior transitions are found to be irreversible, exhibiting a form of hysteresis, while others are fully reversible. Certain morphologies are shown to behave like simplistic sequential logic circuits, indicating that the system has a form of memory encoded into the morphology-environment interactions. Investigating the limits of how morphology-environment interactions induce non-trivial behaviors is a key step for the design of embodied artificial life-forms.

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.