Neurophysiological Models of Gaze Control in Humanoid Robotics

Manfredi, Luigi; Maini, Eliseo Stefano; Laschi, Cecilia

doi:10.5772/6728

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…A common representation of a visual map in the SC is given by Ottes et al (1986), where the retinotopic space is mapped to the collicular space using a logpolar transformation. That transformation has been suggested to lie at the core of complex mechanisms of visual attention (Taouali et al, 2015), including saccades (Manfredi et al, 2009).…”

Section: Biological Inspirationmentioning

confidence: 99%

A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect

2022

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Multimodal merging encompasses the ability to localize stimuli based on imprecise information sampled through individual senses such as sight and hearing. Merging decisions are standardly described using Bayesian models that fit behaviors over many trials, encapsulated in a probability distribution. We introduce a novel computational model based on dynamic neural fields able to simulate decision dynamics and generate localization decisions, trial by trial, adapting to varying degrees of discrepancy between audio and visual stimulations. Neural fields are commonly used to model neural processes at a mesoscopic scale—for instance, neurophysiological activity in the superior colliculus. Our model is fit to human psychophysical data of the ventriloquist effect, additionally testing the influence of retinotopic projection onto the superior colliculus and providing a quantitative performance comparison to the Bayesian reference model. While models perform equally on average, a qualitative analysis of free parameters in our model allows insights into the dynamics of the decision and the individual variations in perception caused by noise. We finally show that the increase in the number of free parameters does not result in overfitting and that the parameter space may be either reduced to fit specific criteria or exploited to perform well on more demanding tasks in the future. Indeed, beyond decision or localization tasks, our model opens the door to the simulation of behavioral dynamics, as well as saccade generation driven by multimodal stimulation.

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Section: Biological Inspirationmentioning

confidence: 99%

A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect

2022

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“…The logpolar transformation is a straightforward way of testing these variations in a controlled setting. Additionally, even when the camera sensory space is perfectly regular, it has been suggested that adding a logpolar transformation on top of it could improve gaze control in robots [34].…”

Section: A Bio-inspired Model Of Audiovisual Processingmentioning

confidence: 99%

Combining Manifold Learning and Neural Field Dynamics for Multimodal Fusion

Forest

Quinton

Lefort

2022

2022 International Joint Conference on Neural Networks (IJCNN)

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For interactivity and cost-efficiency purposes, both biological and artificial agents (e.g., robots) usually rely on sets of complementary sensors. Each sensor samples information from only a subset of the environment, with both the subset and the precision of signals varying through time depending on the agent-environment configuration. Agents must therefore perform multimodal fusion to select and filter relevant information by contrasting the shortcomings and redundancies of different modalities. For that purpose, we propose to combine a classical off-the-shelf manifold learning algorithm with dynamic neural fields (DNF), a training-free bio-inspired model of competition amid topologically-encoded information. Through the adaptation of DNF to irregular multimodal topologies, this coupling exhibits interesting properties, promising reliable localizations enhanced by the selection and attentional capabilities of DNF. In particular, the application of our method to audiovisual datasets (with direct ties to either psychophysics or robotics) shows merged perceptions relying on the spatially-dependent precision of each modality, and robustness to irrelevant features.

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A bioinspired autonomous swimming robot as a tool for studying goal-directed locomotion

et al. 2013

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The bioinspired approach has been key in combining the disciplines of robotics with neuroscience in an effective and promising fashion. Indeed, certain aspects in the field of neuroscience, such as goal-directed locomotion and behaviour selection, can be validated through robotic artefacts. In particular, swimming is a functionally important behaviour where neuromuscular structures, neural control architecture and operation can be replicated artificially following models from biology and neuroscience. In this article, we present a biomimetic system inspired by the lamprey, an early vertebrate that locomotes using anguilliform swimming. The artefact possesses extra- and proprioceptive sensory receptors, muscle-like actuation, distributed embedded control and a vision system. Experiments on optimised swimming and on goal-directed locomotion are reported, as well as the assessment of the performance of the system, which shows high energy efficiency and adaptive behaviour. While the focus is on providing a robotic platform for testing biological models, the reported system can also be of major relevance for the development of engineering system applications.

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Neurophysiological Models of Gaze Control in Humanoid Robotics

Cited by 3 publications

References 43 publications

A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect

A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect

Combining Manifold Learning and Neural Field Dynamics for Multimodal Fusion

A bioinspired autonomous swimming robot as a tool for studying goal-directed locomotion

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