Control of rhythmic behavior: Central and Peripheral Influences to pattern Generation

Hoinville T, Schilling M, Cruse H. Control of rhythmic behavior: Central and Peripheral Influences to pattern Generation. In: ICRA 2015 CPG Workshop : CPGs for Locomotion Control: Pros, Cons & Alternatives. 2015

A hexapod walker using a heterarchical architecture for action selection

Schilling M, Paskarbeit J, Hoinville T, et al. A hexapod walker using a heterarchical architecture for action selection. Frontiers in Computational Neuroscience. 2013;7:126.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

Integrative Biomimetics of Autonomous Hexapedal Locomotion

Dürr V, Arena PP, Cruse H, et al. Integrative Biomimetics of Autonomous Hexapedal Locomotion. Frontiers in Neurorobotics. 2019;13: 88.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

Evolution de contrôleurs neuronaux plastiques (de la locomotion adaptée vers la locomotion adaptative)

Evolutionary robotics mainly focused on evolving neural controllers that are structurally and parametrically fixed, for the control of robots that can roll, walk, swim or fly. This approach led to the design of controllers that are well adapted to constant environments, but not adaptive to varying conditions. To tackle this issue, some researchers suggest to evolve plastic, rather than fixed, neural controllers. Our work follows this way and aims to design plastic neural controllers for legged robots subject to external perturbations, as well as possible mechanical damages.First, we propose a review of the main known forms of neuronal plasticity and their modeling. This review is mostly intended to the roboticists audience. Then, we draw a state of the art of evolving plastic neural controllers and criticize the biological realism of the developed models.On this background, we provide a first contribution centered on thedilemma of evolving both flexible and stable neural controllers. Thus,we suggest to use homeostatic constraints to stabilize CTRNNs incorporating adaptive synapses. We applied this method with success to the locomotion of a one-legged robot, confronted to an external perturbation.Finally, we present a second work based on the knowledge acquired on the biological central pattern generators (CPG) and their plasticity. In practice, we evolve neural relaxation oscillators subject to neuromodulation, for the adaptive locomotion of a modular myriapod robot, that could experience leg amputations.Les recherches menées en robotique évolutionniste se sont avant tout focalisées sur l'évolution de contrôleurs neuronaux structurellement et paramétriquement figés, pour la locomotion de robots qui roulent, marchent, nagent ou volent. Cette démarche a permis la conception de contrôleurs bien adaptés à des environnements constants, mais non adaptatifs aux variations de ceux-ci. Pour y remédier, certains roboticiens ont suggéré de faire évoluer des neuro-contrôleurs non plus figés, mais plastiques. Notre approche s'inscrit dans ce revirement et vise à ce que les robots à pattes puissent adapter leur locomotion aussi bien aux perturbations extérieures, qu'aux éventuelles détériorations de leurs structures mécaniques.Nous proposons en premier lieu une revue des phénomènes de plasticiténeuronale et de leur modélisation, destinée essentiellement aux roboticiens. Nous dressons ensuite un état de l'art de l'évolution de neuro-contrôleurs plastiques et critiquons la plausibilité biologique des modèles développés.Notre première contribution s'inspire des travaux de la robotique évolutionniste et aborde le dilemme de l'évolution de contrôleurs à la fois flexibles et stables. Ainsi, nous employons des contraintes homéostatiques pour stabiliser la plasticité de contrôleurs assurant la locomotion d'un robot monopode confronté à une perturbation freinant son avancée.Notre deuxième contribution s'inspire des connaissances acquises sur les générateurs centraux de pattern (CPG) et leur plasticité. Ainsi,nous proposons l'évolution d'oscillateurs à relaxation soumis à neuromodulation pour la locomotion adaptative d'un robot myriapode confronté à d'éventuelles amputations de pattes.VERSAILLES-BU Sciences et IUT (786462101) / SudocSudocFranceF

Bridging an Interspecies Gap? Toward Human-Insectoid Robot Interaction

Hoinville T, Krause AF, Schilling M, Cruse H. Bridging an Interspecies Gap? Toward Human-Insectoid Robot Interaction. Presented at the HRI 2014 Workshop on Humans and Robots in Asymmetric Interactions, Bielefeld, Germany.Insect-inspired control approaches often provide adaptive, robust and cheap solutions to common robotic problems. However, insect-like morphologies and sensors greatly dier from human capabilities and may therefore impede intuitive interaction with those machines. How to overcome that very asymmetric human-robot interaction? How to make insectlike mechanisms more transparent to exploit, while keeping their advantages? We rst present practical observations focusing on an insectoid robot equipped with active antennae, and then expose verbal communication concepts for embodied cognition. Finally, we discuss how to combine insect-like processes intelligible to humans (e.g. visuotactile attention) with capabilities exceeding those of the insects (e.g. intuitive gestures, verbal communication)

Comparative Study of Two Homeostatic Mechanisms in Evolved Neural Controllers for Legged Locomotion

International audienceThis paper presents a preliminary study on the advantages of two bio-inspired homeostatic mechanisms in neural controllers of legged robots. We consider a robot made up of one leg of 3 dof pushing a body that is sliding on a rail with a friction force. The synthesis of the controller is done by an evolutionary algorithm which choose to attach to each synapse a particular plastic law. Four models of network incorporating or not each homeostatic law are proposed. After evolution, effectiveness of each kind of adaptive controllers is compared in term of statistics on a task of controlling the speed of the robot. The robustness to a perturbation generated by the viscous friction is analyzed in term of control. Results show that homeostatic mechanisms increase evolvability, stability and adaptivity of those controllers

Optimal multiguidance integration in insect navigation

Hoinville T, Wehner R. Optimal multiguidance integration in insect navigation. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(11):2824-2829.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

Evolving Plastic Neural Controllers stabilized by Homeostatic Mechanisms for Adaptation to a Perturbation

International audienceThis paper introduces our ongoing work consisting of evolving bio-inspired plastic neural controllers for autonomous robots submitted to various internal and external perturbations: transmission breaking, slippage, leg loss, etc. We propose a classical neuronal model using adaptive synapses and extended with two bio-inspired homeostatic mechanisms. We perform a comparative study of the impact of the two homeostatic mechanisms on the evolvability of a neural network controlling a single-legged robot that slides on a rail and that is confronted to an external perturbation. The robot has to achieve a required speed goal given by an operator. Evolved neural controllers are tested on long-term simulations to statistically analyse their stability and adaptivity to the perturbation. Finally, we perform behavioral tests to verify our results on the robot controlled with a sinusoidal input while a perturbation occurs. Results show that homeostatic mechanisms increase evolvability, stability and adaptivity of those controllers

Learning and retrieval of memory elements in a navigation task

Hoinville T, Wehner R, Cruse H. Learning and retrieval of memory elements in a navigation task. In: Proc. Int. Conf. Living Machines. LNAI. Vol 7375. 2012: 120-131.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

Toward a biomimetic Johnston's organ for touch localization

Hermes L, Dürr V, Hoinville T. Toward a biomimetic Johnston's organ for touch localization. Proceedings of the German Zoological Society. 2018: NB 9.Tactile exploration of the near-range environment appears ubiquitous in insects. For instance, walking stick insects continuously move their pair of antennae to find footholds for their front legs. Each antenna bears different types of mechanoreceptors, each potentially contributing to touch localization. Among them, Johnston's organ, a chordotonal organ in the pedicel, probably encode contact-induced vibrations. Extracting tactile information from vibrations is a tempting approach for insectoid robots as it requires less wiring than pressure sensor arrays and, in contrast to static force sensing strategies, does not necessitate lasting contact phases. Theory shows that the first vibration modes, i.e. the lowest natural frequencies, are sufficient to estimate the radial distance of hits along a flexible beam. However, in practice, measuring low-frequency components requires proportionally long signal episodes. Not only the resulting latency would impede timely applications, like locomotion control, but also the damped vibrations may vanish too quickly. Hence, it could be beneficial to exploit higher frequency bands. Since beam theory predicts accurately only the few first vibration modes, we experimentally tested whether and which high-frequency bands could be used to estimate the contact distance along a plastic tube rotated by a servomotor. For a range of contact distances, vibrations were sampled with high rate and sensitivity, using a piezoelectric pickup for acoustic guitar, taped to the base of the antenna. Systematically varied bands of the corresponding power spectra were evaluated by support vector regression. We demonstrate that accurate distance estimates can be obtained from various frequency bands, including high-frequency one