(Georgia Institute of Technology, 2005-07)
Oh, Sang Min; Rehg, James M.; Balch, Tucker; Dellaert, Frank
Switching Linear Dynamic System (SLDS) models are
a popular technique for modeling complex nonlinear dynamic
systems. An SLDS has significantly more descriptive
power than an HMM, but inference in SLDS models is
computationally intractable. This paper describes a novel
inference algorithm for SLDS models based on the Data-
Driven MCMC paradigm. We describe a new proposal
distribution which substantially increases the convergence
speed. Comparisons to standard deterministic approximation
methods demonstrate the improved accuracy of our new
approach. We apply our approach to the problem of learning
an SLDS model of the bee dance. Honeybees communicate
the location and distance to food sources through a
dance that takes place within the hive. We learn SLDS
model parameters from tracking data which is automatically
extracted from video. We then demonstrate the ability to
successfully segment novel bee dances into their constituent
parts, effectively decoding the dance of the bees.
(Georgia Institute of Technology, 2005-04)
Balch, Tucker; Dellaert, Frank; Delmotte, Florent; Khan, Zia; Egerstedt, Magnus B.
In this paper, we study the problem of going
from a real-world, multi-agent system to the generation of
control programs in an automatic fashion. In particular,
a computer vision system is presented, capable of simultaneously
tracking multiple agents, such as social insects.
Moreover, the data obtained from this system is fed into a
mode-reconstruction module that generates low-complexity
control programs, i.e. strings of symbolic descriptions of
control-interrupt pairs, consistent with the empirical data.
The result is a mechanism for going from the real system to
an executable implementation that can be used for controlling
multiple mobile robots.