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Abstract Robotic systems often struggle to adapt to dynamic, unstructured environments due to top-down design constraints based on human assumptions. Inspired by biological morphogenesis, this study introduces a cellular plasticity model based on Turing patterns, enabling multi-cellular robots to self-organize their cell phenotypes in response to environmental stimuli. The model leverages reaction-diffusion dynamics to capture key cellular plasticity phenomena observed in muscle cells, neurons, and stem cells. Analytical analysis explores equilibrium points, stability, and conditions for emergent Turing patterns, while simulations examine parametric influences on system behavior. Physical experiments with the Loopy platform demonstrate that its cells dynamically self-organize mechanical properties in response to behavioral and environmental demands. This response enables Loopy to achieve similar performance to empirically optimized static parameters in obstacle-free environments and outperform the static configuration in an environment with limited space. This work advances morphogenetic robotics, presenting a scalable framework for decentralized, dynamic adaptation in unmodeled environments. 

Abstract This article is a historical perspective on how the study of the neuromechanics of insects and other arthropods has inspired the construction, and especially the control, of hexapod robots. Many hexapod robots’ control systems share common features, including: 1. Direction of motor output of each joint (i.e. to flex or extend) in the leg is gated by an oscillatory or bistable gating mechanism; 2. The relative phasing between each joint is influenced by proprioceptive feedback from the periphery (e.g. joint angles, leg load) or central connections between joint controllers; and 3. Behavior can be directed (e.g. transition from walking along a straight path to walking along a curve) via low-dimensional, broadly-acting descending inputs to the network. These distributed control schemes are inspired by, and in some robots, closely mimic the organization of the nervous systems of insects, the natural hexapods, as well as crustaceans. Nearly a century of research has revealed organizational principles such as central pattern generators, the role of proprioceptive feedback in control, and command neurons. These concepts have inspired the control systems of hexapod robots in the past, in which these structures were applied to robot controllers with neuromorphic (i.e. distributed) organization, but not neuromorphic computational units (i.e. neurons) or computational hardware (i.e. hardware-accelerated neurons). Presently, several hexapod robots are controlled with neuromorphic computational units with or without neuromorphic organization, almost always without neuromorphic hardware. In the near future, we expect to see hexapod robots whose controllers include neuromorphic organization, computational units, and hardware. Such robots may exhibit the full mobility of their insect counterparts thanks to a ‘biology-first’ approach to controller design. This perspective article is not a comprehensive review of the neuroscientific literature but is meant to give those with engineering backgrounds a gentle introduction into the neuroscientific principles that underlie models and inspire neuromorphic robot controllers. A historical summary of hexapod robots whose control systems and behaviors use neuromorphic elements is provided. Robots whose controllers closely model animals and may be used to generate concrete hypotheses for future animal experiments are of particular interest to the authors. The authors hope that by highlighting the decades of experimental research that has led to today’s accepted organization principles of arthropod nervous systems, engineers may better understand these systems and more fully apply biological details in their robots. To assist the interested reader, deeper reviews of particular topics from biology are suggested throughout. 

Survival analysis is a crucial statistical technique used to estimate the anticipated duration until a specific event occurs. However, current methods often involve discretizing the time scale and struggle with managing absent features within the data. This becomes especially pertinent since events can transpire at any given point, rendering event analysis a continuous concern. Additionally, the presence of missing attributes within tabular data is widespread. By leveraging recent developments of Transformer and Self-Supervised Learning (SSL), we introduce SSL-SurvFormer. This entails a continuously monotonic Transformer network, empowered by SSL pre-training, that is designed to address the challenges presented by continuous events and absent features in survival prediction. Our proposed continuously monotonic Transformer model facilitates accurate estimation of survival probabilities, thereby bypassing the need for temporal discretization. Additionally, our SSL pre-training strategy incorporates data transformation to adeptly manage missing information. The SSL pre-training encompasses two tasks: mask prediction, which identifies positions of absent features, and reconstruction, which endeavors to recover absent elements based on observed ones. Our empirical evaluations conducted across a variety of datasets, including FLCHAIN, METABRIC, and SUPPORT, consistently highlight the superior performance of SSL-SurvFormer in comparison to existing methods. Additionally, SSL-SurvFormer demonstrates effectiveness in handling missing values, a critical aspect often encountered in real-world datasets.