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The future of the CDM Rea ding Group. 1. The problem with the current arrangement What we want to do How we will do it How to maintain focus. CDM started with three major areas identified

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Presentation Transcript
The problem with the current arrangement
  • What we want to do
  • How we will do it
  • How to maintain focus
CDM started with three major areas identified

Adaptation - focusing on elements of learning and decision making for robot platforms to deal with adaptation and model/environment uncertainty.

Collaboration - Learning how to collaborate and learning joint payoffs with and without prior model knowledge of the decision structure.

Structure - Formulating and solving the control/decision structures for multi robot systems and large scale robot networks.

Big, woolly concepts that led to dispersion of ideas

We need to use this group to think about problems, focus on solutions and importantly to discuss


We should also be actively pursuing new ides, learning new methods and attempting to apply them
  • However, maintaining coverage of current research is important, so we will continue to review papers presented by each member of the group
from next week

Paper review



From next week:
  • Meetings will be aimed to run for 1-1.5 hours
  • Each meeting will include a paper review, time for discussion and some sort of tutorial-style learning session
  • Paper reviews will be shortened (aim to run for 20 mins or so)

20 mins

10 mins

60 mins

paper reviews
Paper Reviews
  • For each paper, we should be trying to address the ideas of the paper and summarise their relevance and key findings
  • We would like presentations to fit to a rough template answering the following questions
    • What was the aim of the paper?
    • What methods did they use which already existed, and what new contributions did they make?
    • How are the methods and/or results related to the interests of this group?
    • Any notable features for future papers (good/bad practices)
research session
Research Session
  • I think it would be worthwhile selecting a topic which is:
    • New to many (all) of us
    • Interesting
    • Usefully applicable to robotics problems
  • 6-8 meetings on a single topic. Less than 1 university semester but should allow us to cover enough breadth and depth to realise how useful it is and what parts apply to our problems
research session1
Research Session
  • Prefer to study something new
  • Would be great if we had access to an expert
  • Advantage of being in a University is that we have access to many experts, often just need to ask
  • There are many areas of interest, I have selected a few I think might meet the above criteria
  • What it is
  • What learning resources are available
  • How it could be useful
functional analysis
Functional Analysis
  • What is it?
  • “Functional analysis is a branch of mathematical analysis, the core of which is formed by the study of vector spaces endowed with some kind of limit-related structure (e.g. inner product, norm, topology, etc.) and the linear operators acting upon these spaces and respecting these structures in a suitable sense.“
  • Hilbert Spaces, Banach Spaces
  • Fairly fundamental maths – provides some interesting results (Eigen analysis, positive definteness, kernel trick, etc. )
functional analysis1
Functional Analysis
  • Learning Resources
  • A relatively well-studied area of (advanced) mathematics
  • Often taught in later year or honours-level maths, plenty of sets of lecture notes around
  • Lots of textbooks, including from Erwin Kreyszig (of Advanced Engineering Mathematics fame) that assures me:
    • "The book is elementary. A background in undergraduate mathematics, in particular, linear algebra and ordinary calculus, is sufficient as a prerequisite.“
  • Honours course at USyd, maths lecturer who might help
How would we use it?
  • Possibly the main issue with this area
  • We (I) clearly already use many of the results without a proper understanding of the core maths
  • Will we be able to extend any of this work into new areas for robotic applications?
compressed sensing2
Compressed Sensing
  • Learning Resources
  • Lots of papers, lots of descriptions, relatively few accessible works with questions etc.
  • Emmanuel Candés gave two presentations at the 2009 Machine Learning Summer School which might be useful
  • Not sure who we could access with info
How would we use it?
  • There are ridiculous numbers of papers in this area (one of the early papers from 2006 has, according to Google been cited nearly 3000 times =1.6 times a day for five years)
  • Our goal would not be to contribute to the area, but to use results and apply them (in possibly new ways) to robotics problems
  • Ideas:
    • Can we use it in place of other regression techniques?
    • What does it mean for distributed operations?
    • Can it be used for compression with guaranteed reconstructions in communications?
  • Again, have to be careful to keep in mind our original goals
continuous planning
Continuous Planning
  • What is it?
  • Many people in our group are interested in looking at planning problems for motion planning over continuous spaces with continuous (possibly stochastic) actions and attempting to maximise some sort of continuos reward (information, energy, etc)
  • I don’t have a good idea of what we need to know to look at this problem
  • It might be worth discussing to see what it is we actually want to look at here, and how we would do it
continuous planning1
Continuous Planning
  • Learning Resources
  • Depends on what we decide to do
  • How would we use it?
  • Many applications (problem-driven)
other ideas
Other Ideas
  • Langrangian Mechanics
  • Nonlinear dimensionality reduction
  • Convex optimisation (new academic?)
  • Anything else (think of what we want to learn from it, what resources we have available and how you think it might be useful)
having said that
Having said that...
  • I think we could combine parts of these to learn compressed sensing with a reasonable mathematical grounding (it gets ugly very quickly)
  • Basic functional analysis - spaces and metrics, function spaces (lp), Hilbert spaces
  • Kernel PCA – nice examples, useful information, understanding of bases
  • Compressed sensing – Fundamental papers, robotic applications
maintaining focus
Maintaining Focus
  • I get distracted easily
  • A difficult part of this group is going to be maintaining a focus on what we are trying to achieve and keeping members of the group interested at the same time
  • We may need some sort of mechanism to keep us focused (debrief slide, pre-defined goals, ?)