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Spring 2015 GRASP Seminar: Subramanian Ramamoorthy, University of Edinburgh, “Topological Trajectory Classification with Persistent Homology”

January 23, 2015 @ 11:00 am - 12:00 pm

robots that seek to work together with human co-workers must adopt
representations that span the hierarchy from the quantitative sensorimotor
signals to more qualitative task specifications. Despite significant recent
activity around methods for learning hierarchical representations, e.g., in
computer vision, the problem of defining and learning action-oriented symbols remains
somewhat open.

in this way, we address the problem of trajectory
classification. We present a sampling-based approach that achieves this in
general configuration spaces relying only on the availability of collision-free
samples. Unlike previous sampling-based approaches in robotics, which use
graphs to capture information about the path-connectedness of a configuration
space, we construct a multiscale approximation of neighborhoods of the
collision free configurations based on filtrations of simplicial complexes. Our
approach thereby extracts additional homological information, which is
essential for a topological trajectory classification of sets of trajectories
starting and ending in two fixed points. Using a cone construction, we then
further generalize this approach to classify sets of trajectories even when
start and end points are allowed to vary in a path-connected subset. We furthermore
show how an augmented filtration of simplicial complexes based on an arbitrary
function on the configuration space, such as a costmap, can be defined to
incorporate additional constraints. We evaluate this in up to 6-dim configuration
spaces, in simulation as well as real world experiments with the Baxter and PR2

view this work as a step towards a broader family of algorithms that maintain
beliefs and plan actions in a multiscale fashion. I will conclude my talk with
a discussion on this, in the process giving a high level outline of some
related algorithms from our recent work addressing interactive decision making,
including one based on a stochastic game-theoretic formulation of the problem
of learning to interact with other agents without prior coordination.


- Learn More

Dr. Subramanian
Ramamoorthy is a Reader (Associate Professor) in the School of
Informatics, University of Edinburgh, where he has been since 2007. He is
Coordinator of the EPSRC Robotarium Research Facility, and Executive Committee
Member for the Centre for Doctoral Training in Robotics and Autonomous Systems.
Previously, he received a PhD in Electrical and Computer Engineering from The
University of Texas at Austin. He is a Member of the Young Academy of Scotland
at the Royal Society of Edinburgh.

His current research
is focussed on problems of autonomous learning and decision-making under
uncertainty, by long-lived agents and agent teams interacting within dynamic
environments. This work is motivated by applications in autonomous robotics,
human-robot interaction, intelligent interfaces and other autonomous agents in
mixed human-machine environments. These problems are solved using a combination
of methods involving layered representations based on geometric/topological
abstractions, game theoretic and behavioural models of inter-dependent decision
making, and machine learning with emphasis on issues of transfer, online and reinforcement

His work has been
recognised by nominations for Best Paper Awards at major international
conferences – ICRA 2008, IROS 2010, ICDL 2012 and EACL 2014. He serves in
editorial and programme committee roles for conferences and journals in the
areas of AI and Robotics. He leads Team Edinferno, the first UK entry in the
Standard Platform League at the RoboCup International Competition. This work
has received media coverage, including by BBC News and The Telegraph, and has
resulted in many public engagement activities, such as at the Royal Society
Summer Science Exhibition, Edinburgh International Science festival and
Edinburgh Festival Fringe.

joining the School of Informatics, he was a Staff Engineer with National
Instruments Corp., where he contributed to five products in the areas of motion
control, computer vision and dynamic simulation. This work resulted in seven US
patents and numerous industry awards for product innovation.


January 23, 2015
11:00 am - 12:00 pm
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