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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.

 

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.

 

Despite the impressive performance of recent marine robots, many of their components are non‐biodegradable or even toxic and may negatively impact sensitive ecosystems. To overcome these limitations, biologically‐sourced hydrogels are a candidate material for marine robotics. Recent advances in embedded 3D printing have expanded the design freedom of hydrogel additive manufacturing. However, 3D printing small‐scale hydrogel‐based actuators remains challenging. In this study, Free form reversible embedding of suspended hydrogels (FRESH) printing is applied to fabricate small‐scale biologically‐derived, marine‐sourced hydraulic actuators by printing thin‐wall structures that are water‐tight and pressurizable. Calcium‐alginate hydrogels are used, a sustainable biomaterial sourced from brown seaweed. This process allows actuators to have complex shapes and internal cavities that are difficult to achieve with traditional fabrication techniques. Furthermore, it demonstrates that fabricated components are biodegradable, safely edible, and digestible by marine organisms. Finally, a reversible chelation‐crosslinking mechanism is implemented to dynamically modify alginate actuators' structural stiffness and morphology. This study expands the possible design space for biodegradable marine robots by improving the manufacturability of complex soft devices using biologically‐sourced materials.

 

Small cursorial birds display remarkable walking skills and can negotiate complex and unstructured terrains with ease. The neuromechanical control strategies necessary to adapt to these challenging terrains are still not well understood. Here, we analyzed the 2D- and 3D pelvic and leg kinematic strategies employed by the common quail to negotiate visible steps (upwards and downwards) of about 10%, and 50% of their leg length. We used biplanar fluoroscopy to accurately describe joint positions in three dimensions and performed semi-automatic landmark localization using deep learning. Quails negotiated the vertical obstacles without major problems and rapidly regained steady-state locomotion. When coping with step upwards, the quail mostly adapted the trailing limb to permit the leading leg to step on the elevated substrate similarly as it did during level locomotion. When negotiated steps downwards, both legs showed significant adaptations. For those small and moderate step heights that did not induce aerial running, the quail kept the kinematic pattern of the distal joints largely unchanged during uneven locomotion, and most changes occurred in proximal joints. The hip regulated leg length, while the distal joints maintained the spring-damped limb patterns. However, to negotiate the largest visible steps, more dramatic kinematic alterations were observed. There all joints contributed to leg lengthening/shortening in the trailing leg, and both the trailing and leading legs stepped more vertically and less abducted. In addition, locomotion speed was decreased. We hypothesize a shift from a dynamic walking program to more goal-directed motions that might be focused on maximizing safety.

 

One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control. 

Sport-related injuries to articular structures often alter the sensory information conveyed by joint structures to the nervous system. However, the role of joint sensory afferents in motor control is still unclear. Here, we evaluate the role of knee joint sensory afferents in the control of quadriceps muscles, hypothesizing that such sensory information modulates control strategies that limit patellofemoreal joint loading. We compared locomotor kinematics and muscle activity before and after inhibition of knee sensory afferents by injection of lidocaine into the knee capsule of rats. We evaluated whether this inhibition reduced the strength of correlation between the activity of vastus medialis (VM) and vastus lateralis (VL) both across strides and within each stride, coordination patterns that limit net mediolateral patellofemoral forces. We also evaluated whether this inhibition altered correlations among the other quadriceps muscle activity, the time-profiles of individual EMG envelopes, or movement kinematics. Neither the EMG envelopes nor limb kinematics was affected by the inhibition of knee sensory afferents. This perturbation also did not affect the correlations between VM and VL, suggesting that the regulation of patellofemoral joint loading is mediated by different mechanisms. However, inhibition of knee sensory afferents caused a significant reduction in the correlation between vastus intermedius (VI) and both VM and VL across, but not within, strides. Knee joint sensory afferents may therefore modulate the coordination between the vasti muscles but only at coarse time scales. Injuries compromising joint afferents might result in altered muscle coordination, potentially leading to persistent internal joint stresses and strains. NEW & NOTEWORTHY Sensory afferents originating from knee joint receptors provide the nervous system with information about the internal state of the joint. In this study, we show that these sensory signals are used to modulate the covariations among the activity of a subset of vasti muscles across strides of locomotion. Sport-related injuries that damage joint receptors may therefore compromise these mechanisms of muscle coordination, potentially leading to persistent internal joint stresses and strains. 

Abstract Locusts ( Schistocerca gregaria ) jump using a latch mediated spring actuated system in the femur-tibia joint of their metathoracic legs. These jumps are exceptionally fast and display angular rotation immediately after take-off. In this study, we focus on the angular velocity, at take-off, of locusts ranging between 0.049 and 1.50 g to determine if and how rotation-rate scales with size. From 263 jumps recorded from 44 individuals, we found that angular velocity scales with mass −0.33 , consistent with a hypothesis of locusts having a constant rotational kinetic energy density. Within the data from each locust, angular velocity increased proportionally with linear velocity, suggesting the two cannot be independently controlled and thus a fixed energy budget is formed at take-off. On average, the energy budget of a jump is distributed 98.7% to translational kinetic energy and gravitational potential energy, and 1.3% to rotational kinetic energy. The percentage of energy devoted to rotation was constant across all sizes of locusts and represents a very small proportion of the energy budget. This analysis suggests that smaller locusts find it harder to jump without body rotation. 

Abstract Ultrafast movements propelled by springs and released by latches are thought limited to energetic adjustments prior to movement, and seemingly cannot adjust once movement begins. Even so, across the tree of life, ultrafast organisms navigate dynamic environments and generate a range of movements, suggesting unrecognized capabilities for control. We develop a framework of control pathways leveraging the non-linear dynamics of spring-propelled, latch-released systems. We analytically model spring dynamics and develop reduced-parameter models of latch dynamics to quantify how they can be tuned internally or through changing external environments. Using Lagrangian mechanics, we test feedforward and feedback control implementation via spring and latch dynamics. We establish through empirically-informed modeling that ultrafast movement can be controllably varied during latch release and spring propulsion. A deeper understanding of the interconnection between multiple control pathways, and the tunability of each control pathway, in ultrafast biomechanical systems presented here has the potential to expand the capabilities of synthetic ultra-fast systems and provides a new framework to understand the behaviors of fast organisms subject to perturbations and environmental non-idealities.