Abstract
This paper presents novel results of complex action learning experiments based on the use of extended multiple timescales recurrent neural networks (MTRNN). The experiments were carried out with the iCub humanoid robot, as a model of the developmental learning of motor primitives as the basis of sensorimotor and linguistic compositionality. The model was implemented through the Aquila cognitive robotics toolkit, which supports the CUDA architecture and makes use of massively parallel GPUs (graphics processing units). The results presented herein show that the model was able to learn and successfully reproduce multiple behavioural sequences of actions in an object manipulation task scenario using large-scale MTRNNs. This forms the basis on ongoing experiments on action and language compositionality
Highlights
Humans are able acquire many skilled behaviours during their life-times
This paper presents preliminary results of complex action learning based on an multiple time-scales recurrent neural network (MTRNN) model embodied in the iCub humanoid robot
The differential equation 4 describes the calculation of neural activities over time where ui,t is the membrane potential, xj,t is the activity of jth neuron, wij correspond to synaptic connections from the jth to the ith neuron and the τ parameter that defines the decay rate of ith neuron
Summary
Humans are able acquire many skilled behaviours during their life-times. Learning complex behaviours is achieved through a constant repetition of the same movements over and over while certain components are segmented into reusable elements known as motor primitives. The experimental results showed that this can be achieved by making the same movement repeatedly, which allows the neural system to estimate and compensate for the Coriolis forces generated by a moving reference plane These studies are clearly demonstrating the robustness and the flexibility of human motor control system capable of exploiting the use of motor primitives in order to reach higher level goals. For example MOSAIC [15] or mixture of multiple RNN expert systems [16], implemented functional hierarchies via explicit hierarchical structure where the motor primitives are represented through the local lowlevel modules whereas the higher-level modules were in charge of recombining these primitives using extra mechanisms such as gate selection systems. The model was implemented as part of Aquila cognitive robotics toolkit [24] and accelerated through the CUDA architecture making use of massively parallel GPU devices that significantly outperform standard CPU processors on parallel tasks
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
