563 research outputs found
A Depth-Based Algorithm for Manipulating Deformable Objects Using Smooth Parametric Surfaces and Energy Minimisation
International audienceIn this brief work, we present a new method for controlling deformations of soft objects by using parametric surfaces as a new type of deformation feedback features. This new approach allows us to actively deform objects into complex 3D shapes. A kinematic-based motion controller is derived using an energy minimisation strategy
A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-based Manipulation Tasks
While the original goal for developing robots is replacing humans in
dangerous and tedious tasks, the final target shall be completely mimicking the
human cognitive and motor behaviour. Hence, building detailed computational
models for the human brain is one of the reasonable ways to attain this. The
cerebellum is one of the key players in our neural system to guarantee
dexterous manipulation and coordinated movements as concluded from lesions in
that region. Studies suggest that it acts as a forward model providing
anticipatory corrections for the sensory signals based on observed
discrepancies from the reference values. While most studies consider providing
the teaching signal as error in joint-space, few studies consider the error in
task-space and even fewer consider the spiking nature of the cerebellum on the
cellular-level. In this study, a detailed cellular-level forward cerebellar
model is developed, including modeling of Golgi and Basket cells which are
usually neglected in previous studies. To preserve the biological features of
the cerebellum in the developed model, a hyperparameter optimization method
tunes the network accordingly. The efficiency and biological plausibility of
the proposed cerebellar-based controller is then demonstrated under different
robotic manipulation tasks reproducing motor behaviour observed in human
reaching experiments
On Model Adaptation for Sensorimotor Control of Robots
International audienceIn this expository article, we address the problem of computing adaptive models that can be used for guiding the motion of robotic systems with uncertain action-to-perception relations. The formulation of the uncalibrated sensor-based control problem is first presented, then, various methods for building adaptive sensorimotor models are derived and analysed. Finally, the proposed methodology is exemplified with two cases of study
Vision-Based Control for Robots by a Fully Spiking Neural System Relying on Cerebellar Predictive Learning
The cerebellum plays a distinctive role within our motor control system to
achieve fine and coordinated motions. While cerebellar lesions do not lead to a
complete loss of motor functions, both action and perception are severally
impacted. Hence, it is assumed that the cerebellum uses an internal forward
model to provide anticipatory signals by learning from the error in sensory
states. In some studies, it was demonstrated that the learning process relies
on the joint-space error. However, this may not exist. This work proposes a
novel fully spiking neural system that relies on a forward predictive learning
by means of a cellular cerebellar model. The forward model is learnt thanks to
the sensory feedback in task-space and it acts as a Smith predictor. The latter
predicts sensory corrections in input to a differential mapping spiking neural
network during a visual servoing task of a robot arm manipulator. In this
paper, we promote the developed control system to achieve more accurate target
reaching actions and reduce the motion execution time for the robotic reaching
tasks thanks to the cerebellar predictive capabilities.Comment: 7 pages, 8 figures, 1 tabl
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