Template for the neural control of directed stepping generalized to all legs of MantisBot

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

Neurodynamic modeling of the fruit fly Drosophila melanogaster

This manuscript describes neuromechanical modeling of the fruit fly Drosophila melanogaster in the form of a hexapod robot, Drosophibot, and an accompanying dynamic simulation. Drosophibot is a testbed for real-time dynamical neural controllers modeled after the anatomy and function of the insect nervous system. As such, Drosophibot has been designed to capture features of the animal's biomechanics in order to better test the neural controllers. These features include: dynamically scaling the robot to match the fruit fly by designing its joint elasticity and movement speed; a biomimetic actuator control scheme that converts neural activity into motion in the same way as observed in insects; biomimetic sensing, including proprioception from all leg joints and strain sensing from all leg segments; and passively compliant tarsi that mimic the animal's passive compliance to the walking substrate. We incorporated these features into a dynamical simulation of Drosophibot, and demonstrate that its actuators and sensors perform in an animal-like way. We used this simulation to test a neural walking controller based on anatomical and behavioral data from insects. Finally, we describe Drosophibot's hardware and show that the animal-like features of the simulation transfer to the physical robot.

A neuromechanical model exploring the role of the common inhibitor motor neuron in insect locomotion

In this work, we analyze a simplified, dynamical, closed-loop, neuromechanical simulation of insect joint control. We are specifically interested in two elements: (1) how slow muscle fibers may serve as temporal integrators of sensory feedback and (2) the role of common inhibitory (CI) motor neurons in resetting this integration when the commanded position changes, particularly during steady-state walking. Despite the simplicity of the model, we show that slow muscle fibers increase the accuracy of limb positioning, even for motions much shorter than the relaxation time of the fiber; this increase in accuracy is due to the slow dynamics of the fibers; the CI motor neuron plays a critical role in accelerating muscle relaxation when the limb moves to a new position; as in the animal, this architecture enables the control of the stance phase speed, independent of swing phase amplitude or duration, by changing the gain of sensory feedback to the stance phase muscles. We discuss how this relates to other models, and how it could be applied to robotic control.

Body side-specific changes in sensorimotor processing of movement feedback in a walking insect

Feedback from load and movement sensors can modify timing and magnitude of the motor output in the stepping stick insect. One source of feedback is stretch reception by the femoral chordotonal organ (fCO), which encodes such parameters as the femorotibial (FTi) joint angle, the angular velocity, and its acceleration. Stimulation of the fCO causes a postural resistance reflex, during quiescence, and can elicit the opposite, so-called active reaction (AR), which assists ongoing flexion during active movements. In the present study, we investigated the role of fCO feedback for the difference in likelihood of generating ARs on the inside vs. the outside during curve stepping. We analyzed the effects of fCO stimulation on the motor output to the FTi and the neighboring coxa-trochanter and thorax-coxa joints of the middle leg. In inside and outside turns, the probability for ARs increases with increasing starting angle and decreasing stimulus velocity; furthermore, it is independent of the total angular excursion. However, the transition between stance and swing motor activity always occurs after a specific angular excursion, independent of the turning direction. Feedback from the fCO also has an excitatory influence on levator trochanteris motoneurons (MNs) during inside and outside turns, whereas the same feedback affects protractor coxae MNs only during outside steps. Our results suggest joint- and body side-dependent processing of fCO feedback. A shift in gain may be responsible for different AR probabilities between inside and outside turning, whereas the general control mechanism for ARs is unchanged.NEW & NOTEWORTHY We show that parameters of movement feedback from the tibia in an insect during curve walking are processed in a body side-specific manner, and how. From our results it is highly conceivable that the difference in motor response to the feedback supports the body side-specific leg kinematics during turning. Future studies will need to determine the source for the inputs that determine the local changes in sensory-motor processing.

A Synthetic Nervous System Controls a Simulated Cockroach

The purpose of this work is to better understand how animals control locomotion. This knowledge can then be applied to neuromechanical design to produce more capable and adaptable robot locomotion. To test hypotheses about animal motor control, we model animals and their nervous systems with dynamical simulations, which we call synthetic nervous systems (SNS). However, one major challenge is picking parameter values that produce the intended dynamics. This paper presents a design process that solves this problem without the need for global optimization. We test this method by selecting parameter values for SimRoach2, a dynamical model of a cockroach. Each leg joint is actuated by an antagonistic pair of Hill muscles. A distributed SNS was designed based on pathways known to exist in insects, as well as hypothetical pathways that produced insect-like motion. Each joint’s controller was designed to function as a proportional-integral (PI) feedback loop and tuned with numerical optimization. Once tuned, SimRoach2 walks through a simulated environment, with several cockroach-like features. A model with such reliable low-level performance is necessary to investigate more sophisticated locomotion patterns in the future.

A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion

A dynamical model of an animal's nervous system, or synthetic nervous system (SNS), is a potentially transformational control method. Due to increasingly detailed data on the connectivity and dynamics of both mammalian and insect nervous systems, controlling a legged robot with an SNS is largely a problem of parameter tuning. Our approach to this problem is to design functional subnetworks that perform specific operations, and then assemble them into larger models of the nervous system. In this paper, we present networks that perform addition, subtraction, multiplication, division, differentiation, and integration of incoming signals. Parameters are set within each subnetwork to produce the desired output by utilizing the operating range of neural activity, R, the gain of the operation, k, and bounds based on biological values. The assembly of large networks from functional subnetworks underpins our recent results with MantisBot.