Manoonpong, Poramate

[["dc.bibliographiccitation.artnumber","10"],["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Dasgupta, Sakyasingha"],["dc.contributor.author","Goldschmidt, Dennis"],["dc.contributor.author","Woergoetter, Florentin"],["dc.contributor.author","Manoonpong, Poramate"],["dc.date.accessioned","2018-11-07T09:51:28Z"],["dc.date.available","2018-11-07T09:51:28Z"],["dc.date.issued","2015"],["dc.description.abstract","Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots."],["dc.identifier.doi","10.3389/fnbot.2015.00010"],["dc.identifier.isi","000370403400001"],["dc.identifier.pmid","26441629"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13198"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35924"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media Sa"],["dc.relation.issn","1662-5218"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","20"],["dc.bibliographiccitation.journal","Frontiers in neurorobotics"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Goldschmidt, Dennis"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Dasgupta, Sakyasingha"],["dc.date.accessioned","2019-07-09T11:44:54Z"],["dc.date.available","2019-07-09T11:44:54Z"],["dc.date.issued","2017"],["dc.description.abstract","Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control-enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates."],["dc.identifier.doi","10.3389/fnbot.2017.00020"],["dc.identifier.pmid","28446872"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14958"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59123"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","Frontiers Media S.A."],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/732266/EU//Plan4Act"],["dc.relation.eissn","1662-5218"],["dc.relation.issn","1662-5218"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","006"],["dc.subject.ddc","573"],["dc.subject.ddc","612"],["dc.title","A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","3"],["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Goldschmidt, Dennis"],["dc.contributor.author","Manoonpong, Poramate"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2018-11-07T09:44:55Z"],["dc.date.available","2018-11-07T09:44:55Z"],["dc.date.issued","2014"],["dc.description.abstract","Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment."],["dc.identifier.doi","10.3389/fnbot.2014.00003"],["dc.identifier.isi","000348807700001"],["dc.identifier.pmid","24523694"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11617"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34504"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1662-5218"],["dc.relation.issn","1662-5218"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]