Thierry Hoinville

Walknet, a bio-inspired controller for hexapod walking

Walknet comprises an artificial neural network that allows for the simulation of a considerable amount of behavioral data obtained from walking and standing stick insects. It has been tested by kinematic and dynamic simulations as well as on a number of six-legged robots. Over the years, various different expansions of this network have been provided leading to different versions of Walknet. This review summarizes the most important biological findings described by Walknet and how they can be simulated. Walknet shows how a number of properties observed in insects may emerge from a decentralized architecture. Examples are the continuum of so-called “gaits,” coordination of up to 18 leg joints during stance when walking forward or backward over uneven surfaces and negotiation of curves, dealing with leg loss, as well as being able following motion trajectories without explicit precalculation. The different Walknet versions are compared to other approaches describing insect-inspired hexapod walking. Finally, we briefly address the ability of this decentralized reactive controller to form the basis for the simulation of higher-level cognitive faculties exceeding the capabilities of insects.

A hexapod walker using a heterarchical architecture for action selection

Moving in a cluttered environment with a six-legged walking machine that has additional body actuators, therefore controlling 22 DoFs, is not a trivial task. Already simple forward walking on a flat plane requires the system to select between different internal states. The orchestration of these states depends on walking velocity and on external disturbances. Such disturbances occur continuously, for example due to irregular up-and-down movements of the body or slipping of the legs, even on flat surfaces, in particular when negotiating tight curves. The number of possible states is further increased when the system is allowed to walk backward or when front legs are used as grippers and cannot contribute to walking. Further states are necessary for expansion that allow for navigation. Here we demonstrate a solution for the selection and sequencing of different (attractor) states required to control different behaviors as are forward walking at different speeds, backward walking, as well as negotiation of tight curves. This selection is made by a recurrent neural network (RNN) of motivation units, controlling a bank of decentralized memory elements in combination with the feedback through the environment. The underlying heterarchical architecture of the network allows to select various combinations of these elements. This modular approach representing an example of neural reuse of a limited number of procedures allows for adaptation to different internal and external conditions. A way is sketched as to how this approach may be expanded to form a cognitive system being able to plan ahead. This architecture is characterized by different types of modules being arranged in layers and columns, but the complete network can also be considered as a holistic system showing emergent properties which cannot be attributed to a specific module.

Steering intermediate courses: desert ants combine information from various navigational routines

A number of systems of navigation have been studied in some detail in insects. These include path integration, a system that keeps track of the straight-line distance and direction travelled on the current trip, the use of panoramic landmarks and scenery for orientation, and systematic searching. A traditional view is that only one navigational system is in operation at any one time, with different systems running in sequence depending on the context and conditions. We review selected data suggesting that often, different navigational cues (e.g., compass cues) and different systems of navigation are in operation simultaneously in desert ant navigation. The evidence suggests that all systems operate in parallel forming a heterarchical network. External and internal conditions determine the weights to be accorded to each cue and system. We also show that a model of independent modules feeding into a central summating device, the Navinet model, can in principle account for such data. No central executive processor is necessary aside from a weighted summation of the different cues and systems. Such a heterarchy of parallel systems all in operation represents a new view of insect navigation that has already been expressed informally by some authors.

Learning and Retrieval of Memory Elements in a Navigation Task

Desert ants when foraging for food, navigate by performing path integration and exploiting landmarks. In an earlier paper, we proposed a decentralized neurocontroller that describes this navigation behavior. As by real ants, landmarks are recognized depending on the context, i.e. only when landmarks belong to the path towards the current goal (food source, home). In this earlier version, neither position nor quality of the food sources can be learnt, the memory is preset. In this article, we present a new version, whose memory elements allow for learning food place vectors and quality. When the agent meets a food source, it updates the quality value, if this source is already known, or stores position and quality, if the source is new. Quality values are used to select food sources to be visited. When one source has a too low quality, the agent also finds a shortcut to another known food source.

Motor flexibility in insects: Adaptive coordination of limbs in locomotion and near-range exploration

In recent years, research on insect motor behaviour―locomotion in particular―has provided a number of important new insights, many of which became possible because of methodological advances in motion capture of unrestrained moving insects. Behavioural analyses have not only backed-up neurophysiological analyses of the underlying mechanisms at work, they have also highlighted the complexity and variability of leg movements in naturalistic, unrestrained behaviour. Here, we argue that the variability of unrestrained motor behaviour should be considered a sign of behavioural flexibility. Assuming that variation of movement-related parameters is governed by neural mechanisms, behavioural analyses can complement neurophysiological investigations, for example by (i) dissociating distinct movement episodes based on functional and statistical grounds, (ii) quantifying when and how transitions between movement episodes occur, and (iii) dissociating temporal and spatial coordination. The present review emphasises the importance of considering the functional diversity of limb movements in insect behaviour. In particular, we highlight the fundamental difference between leg movements that generate interaction forces as opposed to those that do not. On that background, we discuss the spatially continuous modulation of swing movements and the quasi-rhythmic nature of stepping across insect orders. Based on examples of motor flexibility in stick insects, we illustrate the relevance of behaviour-based approaches for computational modelling of a rich and adaptive movement repertoire. Finally, we emphasise the intimate interplay of locomotion and near-range exploration. We propose that this interplay, through continuous integration of distributed, multimodal sensory feedback, is key to locomotor flexibility.

Insect-inspired tactile contour sampling using vibration-based robotic antennae

Compared to vision, active tactile sensing enables animals and robots to perform unambiguous object localization, segmentation and shape recognition. Recently, we proposed a bio-inspired, CPG-based, active antennal control model, so-called Contour-net, which captures essential characteristics of antennal behavior in climbing stick insects. In simulation, this model provides a robust and effective way to trace contours and classify various 3D shapes. Here, we propose a physical robotic implementation of Contour-net using vibration-based active antennae. We show that combining tactile contour tracing with vibration-based distance estimation yields fairly accurate localization of contact events in 3D space.

A load-based mechanism for inter-leg coordination in insects

Animals rely on an adaptive coordination of legs during walking. However, which specific mechanisms underlie coordination during natural locomotion remains largely unknown. One hypothesis is that legs can be coordinated mechanically based on a transfer of body load from one leg to another. To test this hypothesis, we simultaneously recorded leg kinematics, ground reaction forces and muscle activity in freely walking stick insects (Carausius morosus). Based on torque calculations, we show that load sensors (campaniform sensilla) at the proximal leg joints are well suited to encode the unloading of the leg in individual steps. The unloading coincides with a switch from stance to swing muscle activity, consistent with a load reflex promoting the stance-to-swing transition. Moreover, a mechanical simulation reveals that the unloading can be ascribed to the loading of a specific neighbouring leg, making it exploitable for inter-leg coordination. We propose that mechanically mediated load-based coordination is used across insects analogously to mammals.

Optimal multiguidance integration in insect navigation

Significance The discovery of “place cells,” “grid cells,” and other spatial cells in the rodent’s forebrain has strengthened the idea that animals navigate their home range environments thanks to a “cognitive map.” Tiny-brained insects, like bees, are also thought to use such a centralized metric mental representation. However, downstream optimal combination of two decentralized guidance routines suffices to explain multiple experimental results obtained in bees and ants. We show that these insect navigators behave analogously to particles oriented by a global elastic force and local magnetic forces directed to the goal. As if equipped with both Ariadne’s thread and Hansel-and-Gretel’s pebbles, insects seem to know where to go rather than where they are on a map. In the last decades, desert ants have become model organisms for the study of insect navigation. In finding their way, they use two major navigational routines: path integration using a celestial compass and landmark guidance based on sets of panoramic views of the terrestrial environment. It has been claimed that this information would enable the insect to acquire and use a centralized cognitive map of its foraging terrain. Here, we present a decentralized architecture, in which the concurrently operating path integration and landmark guidance routines contribute optimally to the directions to be steered, with “optimal” meaning maximizing the certainty (reliability) of the combined information. At any one time during its journey, the animal computes a path integration (global) vector and landmark guidance (local) vector, in which the length of each vector is proportional to the certainty of the individual estimates. Hence, these vectors represent the limited knowledge that the navigator has at any one place about the direction of the goal. The sum of the global and local vectors indicates the navigator’s optimal directional estimate. Wherever applied, this decentralized model architecture is sufficient to simulate the results of quite a number of diverse cue-conflict experiments, which have recently been performed in various behavioral contexts by different authors in both desert ants and honeybees. They include even those experiments that have deliberately been designed by former authors to strengthen the evidence for a metric cognitive map in bees.