Groupes de travail
In recent years, interactions between robotics and neurosciences have significantly increased.
On one side, roboticists develop more and more efficient machines, especially in terms of computing power and sensing and actuation capabilites. These robots have increasingly complex mechanical structures and many of them are biologically inspired. However, compared to humans or even much less developed animals, the tasks that can be performed by these robots are still very basic. Most current methods in robotics do not allow to capture the complexity of information to provide robots with a unified multisensory representation of their environment and make them able to plan and execute their actions autonomously while adapting to change. This limitation motivates an increasing number of roboticists to interact with the neuroscience community in order to identify the neurobiological processes that endow animals and humans with such capabilities.
On the other side, neuroscientits try to understand and model the functioning of the central nervous system (CNS) of humans and animals by using many complementary approaches ranging from electrophysiology and imagery to neurology and psychophysics. In this modeling work, the roboticist can provide valuable elements for interpreting experimental data. Indeed, the theoretical approaches and engineering methods, which were developed for the design of autonomous artificial systems, offer a rigorous framework to structure the thinking of neuroscientists. On the other hand, the robots can be used as test-beds by offering a way to implement theoretical models on physical systems.
This exchange between the two disciplines also led some researchers to develop interfaces between the CNS and machines. These interfaces range from implanting stimulators, to restore mobility to paralyzed limbs, to the use of various devices allowing to measure the cortical activity in order to control robotic systems.
The objective of the GT 8 “Robotics and Neuroscience” is to gather the researchers involved in this multidisciplinary adventure in order to strengthen their collaborations and lead to new developments by sharing models and techniques from different backgrounds. This working group will be structured around three themes on which the community is already very active : motor control, perception and learning. More precisely, the following research axis have been identified
- Motor control
- Understanding the functioning of the CNS of human and animals to control bio-inspired robots.
- Using the formalisms and techniques from robotics to contribute to the modeling fof the CNS
- Conception and development of neuro-robot interfaces.
- Multimodal perception, integration and internal representations.
- Understanding perception within a sensorimotor framework in humans and animals.
- Learning
- Understanding and modeling the learning process in humans, especially for motor tasks and
action selection.
- Providing robotics systems with learning and adaption capabilities.
- Improve the mathematical understanding of the learning and adaption algorithms
On one side, roboticists develop more and more efficient machines, especially in terms of computing power and sensing and actuation capabilites. These robots have increasingly complex mechanical structures and many of them are biologically inspired. However, compared to humans or even much less developed animals, the tasks that can be performed by these robots are still very basic. Most current methods in robotics do not allow to capture the complexity of information to provide robots with a unified multisensory representation of their environment and make them able to plan and execute their actions autonomously while adapting to change. This limitation motivates an increasing number of roboticists to interact with the neuroscience community in order to identify the neurobiological processes that endow animals and humans with such capabilities.
On the other side, neuroscientits try to understand and model the functioning of the central nervous system (CNS) of humans and animals by using many complementary approaches ranging from electrophysiology and imagery to neurology and psychophysics. In this modeling work, the roboticist can provide valuable elements for interpreting experimental data. Indeed, the theoretical approaches and engineering methods, which were developed for the design of autonomous artificial systems, offer a rigorous framework to structure the thinking of neuroscientists. On the other hand, the robots can be used as test-beds by offering a way to implement theoretical models on physical systems.
This exchange between the two disciplines also led some researchers to develop interfaces between the CNS and machines. These interfaces range from implanting stimulators, to restore mobility to paralyzed limbs, to the use of various devices allowing to measure the cortical activity in order to control robotic systems.
The objective of the GT 8 “Robotics and Neuroscience” is to gather the researchers involved in this multidisciplinary adventure in order to strengthen their collaborations and lead to new developments by sharing models and techniques from different backgrounds. This working group will be structured around three themes on which the community is already very active : motor control, perception and learning. More precisely, the following research axis have been identified
- Motor control
- Understanding the functioning of the CNS of human and animals to control bio-inspired robots.
- Using the formalisms and techniques from robotics to contribute to the modeling fof the CNS
- Conception and development of neuro-robot interfaces.
- Multimodal perception, integration and internal representations.
- Understanding perception within a sensorimotor framework in humans and animals.
- Learning
- Understanding and modeling the learning process in humans, especially for motor tasks and
action selection.
- Providing robotics systems with learning and adaption capabilities.
- Improve the mathematical understanding of the learning and adaption algorithms
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