944 research outputs found
Respiratory, postural and spatio-kinetic motor stabilization, internal models, top-down timed motor coordination and expanded cerebello-cerebral circuitry: a review
Human dexterity, bipedality, and song/speech vocalization in Homo are reviewed within a motor evolution perspective in regard to 

(i) brain expansion in cerebello-cerebral circuitry, 
(ii) enhanced predictive internal modeling of body kinematics, body kinetics and action organization, 
(iii) motor mastery due to prolonged practice, 
(iv) task-determined top-down, and accurately timed feedforward motor adjustment of multiple-body/artifact elements, and 
(v) reduction in automatic preflex/spinal reflex mechanisms that would otherwise restrict such top-down processes. 

Dual-task interference and developmental neuroimaging research argues that such internal modeling based motor capabilities are concomitant with the evolution of 
(vi) enhanced attentional, executive function and other high-level cognitive processes, and that 
(vii) these provide dexterity, bipedality and vocalization with effector nonspecific neural resources. 

The possibility is also raised that such neural resources could 
(viii) underlie human internal model based nonmotor cognitions. 

Exploiting Multimodal Information in Deep Learning
Humans are good at using multimodal information to perceive and to interact with the world. Such information includes visual, auditory, kinesthetic, etc. Despite the advancement in deep learning using single modality in the past decade, there are relatively fewer works focused on multimodal learning. Even with existing multimodal deep learning works, most of them focus on a small number of modalities. This dissertation will investigate various distinct forms of multi-modal learning: multiple visual modalities as input, audio-visual multimodal input, and visual and proprioceptive (kinesthetic) multimodal input. Specifically, in the first project we investigate synthesizing light fields from a single image and estimated depth. In the second project, we investigate face recognition for unconstrained videos with audio-visual multimodal inputs. Finally, we investigate learning to construct and use tools with visual, proprioceptive and kinesthetic multimodal inputs.
In the first task, we investigate synthesizing light fields with a single RGB image and its estimated depth. Synthesizing novel views (light fields) from a single image is very challenging since the depth information is lost, and depth information is crucial for view synthesis. We propose to use a pre-trained model to estimate the depth, and then fuse the depth information together with the RGB image to generate the light fields. Our experiments showed that multimodal input (RGB image and depth) significantly improved the performance over the single image input.
In the second task, we focus on learning face recognition for low quality videos. For low quality videos such as low-resolution online videos and surveillance videos, recognizing faces based on video frames alone is very challenging. We propose to use audio information in the video clip to aid in the face recognition task. To achieve this goal, we propose Audio-Visual Aggregation Network (AVAN) to aggregate audio features and visual features using an attention mechanism. Empirical results show that our approach using both visual and audio information significantly improves the face recognition accuracy on unconstrained videos.
Finally, in the third task, we propose to use visual, proprioceptive and kinesthetic inputs to learn to construct and use tools. The use of tools in animals indicates high levels of cognitive capability, and, aside from humans, it is observed only in a small number of higher mammals and avian species, and constructing novel tools is an even more challenging task. Learning this task with only visual input is challenging, therefore, we propose to use visual and proprioceptive (kinesthetic) inputs to accelerate the learning. We build a physically simulated environment for tool construction task. We also introduce a hierarchical reinforcement learning approach to learn to construct tools and reach the target, without any prior knowledge.
The main contribution of this dissertation is in the investigation of multiple scenarios where multimodal processing leads to enhanced performance. We expect the specific methods developed in this work, such as the extraction of hidden modalities (depth), use of attention, and hierarchical rewards, to help us better understand multimodal processing in deep learning
On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation
Biological and robotic grasp and manipulation are undeniably similar at the
level of mechanical task performance. However, their underlying fundamental
biological vs. engineering mechanisms are, by definition, dramatically
different and can even be antithetical. Even our approach to each is
diametrically opposite: inductive science for the study of biological systems
vs. engineering synthesis for the design and construction of robotic systems.
The past 20 years have seen several conceptual advances in both fields and the
quest to unify them. Chief among them is the reluctant recognition that their
underlying fundamental mechanisms may actually share limited common ground,
while exhibiting many fundamental differences. This recognition is particularly
liberating because it allows us to resolve and move beyond multiple paradoxes
and contradictions that arose from the initial reasonable assumption of a large
common ground. Here, we begin by introducing the perspective of neuromechanics,
which emphasizes that real-world behavior emerges from the intimate
interactions among the physical structure of the system, the mechanical
requirements of a task, the feasible neural control actions to produce it, and
the ability of the neuromuscular system to adapt through interactions with the
environment. This allows us to articulate a succinct overview of a few salient
conceptual paradoxes and contradictions regarding under-determined vs.
over-determined mechanics, under- vs. over-actuated control, prescribed vs.
emergent function, learning vs. implementation vs. adaptation, prescriptive vs.
descriptive synergies, and optimal vs. habitual performance. We conclude by
presenting open questions and suggesting directions for future research. We
hope this frank assessment of the state-of-the-art will encourage and guide
these communities to continue to interact and make progress in these important
areas
Chaotic exploration and learning of locomotor behaviours
Recent developments in the embodied approach to understanding the generation of
adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic
motor patterns should not be done in isolation from an appreciation, and indeed
exploitation, of neural-body-environment interactions. Utilising spontaneous mutual
entrainment between neural systems and physical bodies provides a useful passage
to the regions of phase space which are naturally structured by the neuralbody-
environmental interactions. A growing body of work has provided evidence
that chaotic dynamics can be useful in allowing embodied systems to spontaneously
explore potentially useful motor patterns. However, up until now there has
been no general integrated neural system that allows goal-directed, online, realtime
exploration and capture of motor patterns without recourse to external monitoring,
evaluation or training methods. For the first time, we introduce such a system
in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics,
for the exploration and learning of the possible locomotion patterns of an articulated
robot of an arbitrary morphology in an unknown environment. The controller
is modelled as a network of neural oscillators which are coupled only through physical
embodiment, and goal directed exploration of coordinated motor patterns is
achieved by a chaotic search using adaptive bifurcation. The phase space of the
indirectly coupled neural-body-environment system contains multiple transient or
permanent self-organised dynamics each of which is a candidate for a locomotion
behaviour. The adaptive bifurcation enables the system orbit to wander through
various phase-coordinated states using its intrinsic chaotic dynamics as a driving
force and stabilises the system on to one of the states matching the given goal
criteria. In order to improve the sustainability of useful transient patterns, sensory
homeostasis has been introduced which results in an increased diversity of motor outputs,
thus achieving multi-scale exploration. A rhythmic pattern discovered by this
process is memorised and sustained by changing the wiring between initially disconnected
oscillators using an adaptive synchronisation method. The dynamical nature
of the weak coupling through physical embodiment allows this adaptive weight learning
to be easily integrated, thus forming a continuous exploration-learning system.
Our result shows that the novel neuro-robotic system is able to create and learn a
number of emergent locomotion behaviours for a wide range of body configurations
and physical environment, and can re-adapt after sustaining damage. The implications
and analyses of these results for investigating the generality and limitations of
the proposed system are discussed
Design of artificial neural oscillatory circuits for the control of lamprey- and salamander-like locomotion using evolutionary algorithms
This dissertation investigates the evolutionary design of oscillatory artificial neural
networks for the control of animal-like locomotion. It is inspired by the neural organÂŹ
isation of locomotor circuitries in vertebrates, and explores in particular the control
of undulatory swimming and walking. The difficulty with designing such controllers
is to find mechanisms which can transform commands concerning the direction and
the speed of motion into the multiple rhythmic signals sent to the multiple actuators
typically involved in animal-like locomotion. In vertebrates, such control mechanisms
are provided by central pattern generators which are neural circuits capable of proÂŹ
ducing the patterns of oscillations necessary for locomotion without oscillatory input
from higher control centres or from sensory feedback. This thesis explores the space of
possible neural configurations for the control of undulatory locomotion, and addresses
the problem of how biologically plausible neural controllers can be automatically generated.Evolutionary algorithms are used to design connectionist models of central pattern
generators for the motion of simulated lampreys and salamanders. This work is inspired
by Ekeberg's neuronal and mechanical simulation of the lamprey [Ekeberg 93]. The
first part of the thesis consists of developing alternative neural controllers for a similar
mechanical simulation. Using a genetic algorithm and an incremental approach, a
variety of controllers other than the biological configuration are successfully developed
which can control swimming with at least the same efficiency. The same method
is then used to generate synaptic weights for a controller which has the observed
biological connectivity in order to illustrate how the genetic algorithm could be used
for developing neurobiological models. Biologically plausible controllers are evolved
which better fit physiological observations than Ekeberg's hand-crafted model. Finally,
in collaboration with Jerome Kodjabachian, swimming controllers are designed using a
developmental encoding scheme, in which developmental programs are evolved which
determine how neurons divide and get connected to each other on a two-dimensional
substrate.The second part of this dissertation examines the control of salamander-like swimming
and trotting. Salamanders swim like lampreys but, on the ground, they switch to a
trotting gait in which the trunk performs a standing wave with the nodes at the girdles.
Little is known about the locomotion circuitry of the salamander, but neurobiologists
have hypothesised that it is based on a lamprey-like organisation. A mechanical simÂŹ
ulation of a salamander-like animat is developed, and neural controllers capable of
exhibiting the two types of gaits are evolved. The controllers are made of two neural
oscillators projecting to the limb motoneurons and to lamprey-like trunk circuitry. By
modulating the tonic input applied to the networks, the type of gait, the speed and
the direction of motion can be varied.By developing neural controllers for lamprey- and salamander-like locomotion, this
thesis provides insights into the biological control of undulatory swimming and walking, and shows how evolutionary algorithms can be used for developing neurobiological
models and for generating neural controllers for locomotion. Such a method could potentially be used for designing controllers for swimming or walking robots, for instance
Behavior finding: Morphogenetic Designs Shaped by Function
Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organismâs morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design âorganicâ structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding
On neuromechanical approaches for the study of biological and robotic grasp and manipulation
abstract: Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.The electronic version of this article is the complete one and can be found online at: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-017-0305-
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