How we interact with our environment largely depends on both the external cues presented by our surroundings and the internal state from within. Internal states are the ever-changing physiological conditions that communicate the immediate survival needs and motivate the animal to behaviorally fulfill them. Satiety level constitutes such a state, and therefore has a dynamic influence on the output behaviors of an animal. In predatory insects like the praying mantis, hunting tactics, grooming, and mating have been shown to change hierarchical organization of behaviors depending on satiety. Here, we analyze behavior sequences of freely hunting praying mantises (Tenodera sinensis) to explore potential differences in sequential patterning of behavior as a correlate of satiety. First, our data supports previous work that showed starved praying mantises were not just more often attentive to prey, but also more often attentive to further prey. This was indicated by the increased time fraction spent in attentive bouts such as prey monitoring, head turns (to track prey), translations (closing the distance to the prey), and more strike attempts. With increasing satiety, praying mantises showed reduced time in these behaviors and exhibited them primarily towards close-proximity prey. Furthermore, our data demonstrates that during states of starvation, the praying mantis exhibits a stereotyped pattern of behavior that is highly motivated by prey capture. As satiety increased, the sequenced behaviors became more variable, indicating a shift away from the necessity of prey capture to more fluid presentations of behavior assembly.

We present MantisBot, a 28 degree of freedom robot controlled by a high-fidelity neural simulation. It is modeled after the mantis, with many degrees of freedom, because we intend to study directed behaviors and leg multi-functionality, such as prey tracking and striking. As a first step, we present a distributed reflexive posture controller. MantisBot maintains posture through a series of reflexes observed in insects, specifically: strain measurements from a leg produce proportional torque commands (reflex A); large or rapidly decreasing leg strains produce a rapid, single “restep” (reflex B); a leg can only restep if its neighboring legs are all under strain (reflex C); and a leg will search for the ground if it does not reach it as expected (reflex D). All of these reflexes contribute to a hardware platform's posture, and are implemented in a highly distributed fashion. The two most distal joints in each leg each has its own central pattern generator (CPG, 12 total), upon which all of these behaviors depend. To achieve the desired dynamics, we implement a control network of conductance-based neurons with persistent sodium channels arranged in a network like the animal may possess in its thoracic ganglia. The result is a robot capable of actively maintaining posture without a centralized planner or body model. In addition, the network implementation is fast, calculating network dynamics 150 times faster than real time.

Cockroach shelter-seeking strategy may look like an undirected random search, but we show that they are attracted to darkened shelters, arriving at a shelter in about half the time it would otherwise take. We were able to identify four statistically significant trends from the behavior of 134 cockroaches in one-minute naïve walking trials with four different arena configurations. By combining these trends into a model, we arrive at an algorithm that significantly directs a simulated agent to a location. This algorithm was then adapted and tested on a small mobile robot equipped with an onboard camera and antenna-like contact sensors.