[Research] Fwd: Thesis Oral: Matthew Tesch

[Jeff Schneider]

Hi guys,

Matt Tesch (co-advised by Howie Choset and I) will do his PhD defense on Monday. 
  If you want to see real machine learning making a real robot do cool things, 
come to the defense!

Jeff.



-------- Original Message --------
Subject: 	Thesis Oral: Matthew Tesch
Date: 	Fri, 15 Nov 2013 10:57:37 -0500
From: 	Suzanne Lyons Muth <lyonsmuth at cmu.edu>
To: 	ri-people at cs.cmu.edu



Date: 25 November 2013
Time: 10:00 a.m.
Place: Newell Simon Hall 3305
Type: Thesis Oral
Who: Matthew Tesch
Topic: Improving Robot Locomotion Through Learning Methods for Expensive
Black-Box Systems

Abstract:
The modular snake robots in Carnegie Mellon’s Biorobotics lab provide an
intriguing platform for research: they have already been shown to excel at a
variety of locomotive tasks and have incredible potential for navigating complex
terrains, but much of that potential remains untapped. Unfortunately, many
techniques commonly used in robotics prove inapplicable to these snake robots.
This is because of the robots complex, multi-modal locomotion dynamics, which
are difficult to model, and their small size and frequent impacts, which
preclude addition of many standard sensors.

The motivation to expand the capabilities of these robots stems from
experiencing several failures and limitations in real world tests. In an
archaeological expedition near the Red Sea, the robot was able to move further
than a human could into a collapsed cave containing four-millenia-old ship
timbers. However, a gradual sandy slope prevented the robot from moving further
and potentially making an archaeological discovery. At a disaster response
training site, the robot was able to navigate a narrow passage underneath a
rubble pile, but was unable to pass over a four inch high piece of wood which
lay across its path once the passage widened.

This thesis addresses the improvement of these capabilities through the
optimization of functions which are expensive (requiring significant time,
money, computation, or other resources), black-box (providing no gradient or
derivative information), need not be convex or linear, and may have many local
optima.  Objectives evaluated through tests on physical robotic systems often
fit these categories.

Several approaches are derived and tested for optimization of snake robot gait
motion, leading to improved locomotion across flat and sloped terrain.
Additional unique challenges posed by robotic systems are addressed, including
stochasticity in the objective, consideration of multiple conflicting
objectives, and the desire to adapt to changing environments.

Although gaits are the motion of choice for traversing long distances over
uniform terrain, real-world environments will rarely be completely uniform.
Instead, complex motions also must be learned and optimized that enable
navigation over complex terrain and large obstacles. To address this challenge,
I describe an approach to record, simplify, and parameterize demonstrated
trajectories from expert and novice users. As the settings which require such
motions usually can only quantify the result of the motion in terms of success
and failure rather than a numerical score, I derive extensions to the
optimization framework used for improving gaits to handle stochastic binary
functions, and use this to optimize robustness of trajectories for moving over
obstacles.

Overall, these algorithms allow snake robot locomotion through any type of
environment to be optimized.  Furthermore, the generality inherent in the
black-box approach allows these techniques to be applicable to a wide variety of
problems in robotics.


Thesis Committee Members:
Howie Choset, Chair
Jeff Schneider
Drew Bagnell
Jared Cohon
Stefan Schaal, University of Southern California


A copy of the thesis document is available at:
http://www.cs.cmu.edu/~mtesch/thesis.pdf