[robotics-worldwide] [jobs] PhD position in RL for humanoid robots

tdahl torbjorn.dahl at plymouth.ac.uk
Fri May 1 06:57:03 PDT 2015


We are looking for PhD student in reinforcement learning for humanoid robots
(Topic 2:  Learning and Planning with Emergent Hierarchical Representations
and Decaying Short-Term Memory) at the Centre for Robotics and Neural
Systems at the University of Plymouth UK.

A brief description of the project is included below.
The application deadline is May 29th, 12noon, UK time.
The project starts October 1st.

Further details on the project, funding and application procedure are
available at:
https://www.plymouth.ac.uk/student-life/your-studies/the-graduate-school/phd-studentship-at-the-centre-for-robotics-and-neural-systems

Prospective candidates are invited to contact the director of studies
directly.

Best regards,
Torbjorn Dahl
____________________________________________________            
Torbjørn S. DAHL, MEng, ACGI, PhD
Lecturer in Software Engineering
Centre for Robotics and Neural Systems
School of Computing and Mathematics, Plymouth University
Drake Circus, Plymouth PL4 8AA, UK
web-site: http://www.tech.plym.ac.uk/SoCCE/crns/staff/dahl/
____________________________________________________

Abstract

This project will develop a new generation of artificial neural networks for
reinforcement learning (RL), significantly increasing the applicability of
RL in areas such as robotics.  The new algorithms will make use of two
central features of biological memory to limit their space and time
requirements; emergent hierarchical representations and decaying short-term
memory (STM). The algorithms developed will use hierarchical memory
structures that grow according to a given RL problem.  The hierarchical
structures will be made from multiple Kohonen networks and will provide the
algorithms’ long-term memory (LTM) capabilities.  The Kohonen networks will
be augmented with decaying node activation values providing explicit STM
capabilities within a connectionist framework.  The main encoding mechanism
in the new algorithms uses the decaying activation values (STM) of one
network layer as input to update the weights (LTM) of other layers.  This
architecture has allowed us to develop new, more efficient, mechanisms for
key RL functions including hidden state identification and future reward
estimation.  We have already demonstrated some of these mechanisms using
analytical methods and lossless memory encoding (Pierris and Dahl, 2014). 
This project will build on those results to produce true connectionist
algorithms that can be parallelised, e.g., using Cuda technology.  The work
will consider traditional RL benchmarks as well as robot learning problems
and will use the University of Plymouth’s Nao or iCub humanoid robots. 

Related reference

Pierris G. and Dahl T. S., Humanoid Tactile Gesture Production using a
Hierarchical SOM-based Encoding. IEEE Transactions on Autonomous Mental
Development, 6(2):153-167, 2014.




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