Visually targeted reaching in horse-head grasshoppers

Kinematics and coordination of moth flies walking on smooth and rough surfaces

The moth fly, Clogmia albipunctata, is a common synanthropic insect with a worldwide range that lives in nearly any area with moist, decaying organic matter. These habitats comprise both smooth, slippery substrates (e.g., bathroom drains) and heterogeneous, bumpy ground (e.g., soil in plant pots). By using terrain of varying levels of roughness, we focus specifically on how substrate roughness at the approximate size scale of the organism affects kinematics and coordination in adult moth flies. Finally, we compare and contrast our characterizations of locomotion in C. albipunctata with previous work of insect walking in naturalistic environments.

Visual guidance fine-tunes probing movements of an insect appendage

Visually guided reaching, a regular feature of human life, comprises an intricate neural control task. It includes identifying the target's position in 3D space, passing the representation to the motor system that controls the respective appendages, and adjusting ongoing movements using visual and proprioceptive feedback. Given the complexity of the neural control task, invertebrates, with their numerically constrained central nervous systems, are often considered incapable of this level of visuomotor guidance. Here, we provide mechanistic insights into visual appendage guidance in insects by studying the probing movements of the hummingbird hawkmoth's proboscis as they search for a flower's nectary. We show that visually guided proboscis movements fine-tune the coarse control provided by body movements in flight. By impairing the animals' view of their proboscis, we demonstrate that continuous visual feedback is required and actively sought out to guide this appendage. In doing so, we establish an insect model for the study of neural strategies underlying eye-appendage control in a simple nervous system.

Flexible locomotion in complex environments: the influence of species, speed and sensory feedback on panarthropod inter-leg coordination

Panarthropods (a clade containing arthropods, tardigrades and onychophorans) can adeptly move across a wide range of challenging terrains and their ability to do so given their relatively simple nervous systems makes them compelling study organisms. Studies of forward walking on flat terrain excitingly point to key features in inter-leg coordination patterns that seem to be 'universally' shared across panarthropods. However, when movement through more complex, naturalistic terrain is considered, variability in coordination patterns - from the intra-individual to inter-species level - becomes more apparent. This variability is likely to be due to the interplay between sensory feedback and local pattern-generating activity, and depends crucially on species, walking speed and behavioral goal. Here, I gather data from the literature of panarthropod walking coordination on both flat ground and across more complex terrain. This Review aims to emphasize the value of: (1) designing experiments with an eye towards studying organisms in natural environments; (2) thoughtfully integrating results from various experimental techniques, such as neurophysiological and biomechanical studies; and (3) ensuring that data is collected and made available from a wider range of species for future comparative analyses.

Decentralized control and local information for robust and adaptive decentralized Deep Reinforcement Learning

Decentralization is a central characteristic of biological motor control that allows for fast responses relying on local sensory information. In contrast, the current trend of Deep Reinforcement Learning (DRL) based approaches to motor control follows a centralized paradigm using a single, holistic controller that has to untangle the whole input information space. This motivates to ask whether decentralization as seen in biological control architectures might also be beneficial for embodied sensori-motor control systems when using DRL. To answer this question, we provide an analysis and comparison of eight control architectures for adaptive locomotion that were derived for a four-legged agent, but with their degree of decentralization varying systematically between the extremes of fully centralized and fully decentralized. Our comparison shows that learning speed is significantly enhanced in distributed architectures-while still reaching the same high performance level of centralized architectures-due to smaller search spaces and local costs providing more focused information for learning. Second, we find an increased robustness of the learning process in the decentralized cases-it is less demanding to hyperparameter selection and less prone to becoming trapped in poor local minima. Finally, when examining generalization to uneven terrains-not used during training-we find best performance for an intermediate architecture that is decentralized, but integrates only local information from both neighboring legs. Together, these findings demonstrate beneficial effects of distributing control into decentralized units and relying on local information. This appears as a promising approach towards more robust DRL and better generalization towards adaptive behavior.

Vision does not impact walking performance in Argentine ants

Many walking insects use vision for long-distance navigation, but the influence of vision on rapid walking performance that requires close-range obstacle detection and directing the limbs towards stable footholds remains largely untested. We compared Argentine ant (Linepithema humile) workers in light versus darkness while traversing flat and uneven terrain. In darkness, ants reduced flat-ground walking speeds by only 5%. Similarly, the approach speed and time to cross a step obstacle were not significantly affected by lack of lighting. To determine whether tactile sensing might compensate for vision loss, we tracked antennal motion and observed shifts in spatiotemporal activity as a result of terrain structure but not illumination. Together, these findings suggest that vision does not impact walking performance in Argentine ant workers. Our results help contextualize eye variation across ants, including subterranean, nocturnal and eyeless species that walk in complete darkness. More broadly, our findings highlight the importance of integrating vision, proprioception and tactile sensing for robust locomotion in unstructured environments.

Prey speed influences the speed and structure of the raptorial strike of a 'sit-and-wait' predator

Predators must often employ flexible strategies to capture prey. Particular attention has been given to the strategies of visual predators that actively pursue their prey, but sit-and-wait predators have been largely overlooked, their strategies often characterized as stereotyped. Praying mantids are primarily sit-and-wait predators that often employ crypsis to catch their prey using a raptorial strike produced by their highly modified forelimbs. Here, we show that the raptorial strike of the Madagascan marbled mantis (Polyspilota aeruginosa) varies in duration from 60 to 290 ms due to the tibial extension alone; slower strikes involve slower tibial extensions that may also be interrupted by a pause. The success of a strike is independent of its duration or the presence of these pauses. However, prey speed affects the duration of tibial extension and the probability of a pause occurring, both increasing at slower prey speeds. Adjusting the duration of the tibial extension according to prey speed allows mantids to time the final downward sweep of the tibia to their prey's approach. The use of visual inputs to adjust the motor pattern controlling forelimb movements shows that not all aspects of the strike are stereotyped and that sit-and-wait predators can produce behavioural flexibility.

Integrative Biomimetics of Autonomous Hexapedal Locomotion

Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots-ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model-we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.

Transfer of Spatial Contact Information Among Limbs and the Notion of Peripersonal Space in Insects

Internal representation of far-range space in insects is well established, as it is necessary for navigation behavior. Although it is likely that insects also have an internal representation of near-range space, the behavioral evidence for the latter is much less evident. Here, we estimate the size and shape of the spatial equivalent of a near-range representation that is constituted by somatosensory sampling events. To do so, we use a large set of experimental whole-body motion capture data on unrestrained walking, climbing and searching behavior in stick insects of the species Carausius morosus to delineate ‘action volumes’ and ‘contact volumes’ for both antennae and all six legs. As these volumes are derived from recorded sampling events, they comprise a volume equivalent to a representation of coinciding somatosensory and motor activity. Accordingly, we define this volume as the peripersonal space of an insect. It is of immediate behavioral relevance, because it comprises all potential external object locations within the action range of the body. In a next step, we introduce the notion of an affordance space as that part of peripersonal space within which contact-induced spatial estimates lie within the action ranges of more than one limb. Because the action volumes of limbs overlap in this affordance space, spatial information from one limb can be used to control the movement of another limb. Thus, it gives rise to an affordance as known for contact-induced reaching movements and spatial coordination of footfall patterns in stick insects. Finally, we probe the computational properties of the experimentally derived affordance space for pairs of neighboring legs. This is done by use of artificial neural networks that map the posture of one leg into a target posture of another leg with identical foot position.

How foresight might support the behavioral flexibility of arthropods

The small brains of insects and other invertebrates are often thought to constrain these animals to live entirely 'in the moment'. In this view, each one of their many seemingly hard-wired behavioral routines is triggered by a precisely defined environmental stimulus configuration, but there is no mental appreciation of the possible outcomes of one's actions, and therefore little flexibility. However, many studies show problem-solving behavior in various arthropod species that falls outside the range of fixed behavior routines. We propose that a basic form of foresight, the ability to predict the outcomes of one's own actions, is at the heart of such behavioral flexibility, and that the evolutionary roots of such outcome expectation are found in the need to disentangle sensory input that is predictable from self-generated motion versus input generated by changes in the outside world. Based on this, locusts, grasshoppers, dragonflies and flies seem to use internal models of the surrounding world to tailor their actions adaptively to predict the imminent future. Honeybees and orb-weaving spiders appear to act towards a desired outcome of their respective constructions, and the genetically pre-programmed routines that govern these constructions are subordinate to achieving the desired goal. Jumping spiders seem to preplan their route to prey suggesting they recognize the spatial challenge and actions necessary to obtain prey. Bumblebees and ants utilize objects not encountered in the wild as types of tools to solve problems in a manner that suggests an awareness of the desired outcome. Here we speculate that it may be simpler, in terms of the required evolutionary changes, computation and neural architecture, for arthropods to recognize their goal and predict the outcomes of their actions towards that goal, rather than having a large number of pre-programmed behaviors necessary to account for their observed behavioral flexibility.

Strength of forelimb lateralization predicts motor errors in an insect

Lateralized behaviours are widespread in both vertebrates and invertebrates, suggesting that lateralization is advantageous. Yet evidence demonstrating proximate or ultimate advantages remains scarce, particularly in invertebrates or in species with individual-level lateralization. Desert locusts (Schistocerca gregaria) are biased in the forelimb they use to perform targeted reaching across a gap. The forelimb and strength of this bias differed among individuals, indicative of individual-level lateralization. Here we show that strongly biased locusts perform better during gap-crossing, making fewer errors with their preferred forelimb. The number of targeting errors locusts make negatively correlates with the strength of forelimb lateralization. This provides evidence that stronger lateralization confers an advantage in terms of improved motor control in an invertebrate with individual-level lateralization.