Research
Publications
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Teaching
Contact
My research has focused on various aspects of learning theory - both
artificial and biological. The projects I have been working on include
research of a more theoretical nature such as statistical machine learning,
functional analysis, optimization theory as well as more experimental and application
oriented real time and online learning in high dimensional movement systems like robots.
Additionally, a third line of my research interest has been towards applying theoretical insights to more biologically relevant
topics of sensorimotor control, visuo-motor learning and sparse neural coding.
The goals of my current research have a bi-directional component to it: 1) to develop
an analytical understanding of learning system capabilities-- going towards development of new
algorithms and efficient solutions for machine learning problems-- with possible inspiration
from biology and 2) to look into statistical modeling of biological information processing equipped
with a deeper understanding of the computational capabilities and limitations of a particular learning
architecture.
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Specific Projects & Topics
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One of the primary foci in my research agenda has been to understand the
analytical and statistical properties of learning systems. The ability
to generalize learned results to a novel situation is a key requirement
for a learning system. The framework of functional analysis
is a good starting point for formalizing such concepts.
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(1) Reproducing Kernel Hilbert Space (RKHS) Based Learning Methods
We have developed a method of formalizing the concept of learning as an inverse
optimization problem employing techniques from functional analysis
and Reproducing Kernel Hilbert Spaces.
[For Related Publications, check
here]
Then, by moving the analysis from the space of training samples to the approximating function space,
we have devised a novel method of directly optimizing the generalization error.
This method -- in contrast to techniques that optimize training error and then perform
regularization to prevent overfitting -- is theoretically sound and provides better control
over implicit assumptions one makes while solving such optimization problems.
This formalization has enabled us to devise efficient methods of performing
exact incremental learning with guaranteed
optimal generalization for a particular solution space.
This research has been a forerunner to the now popular large margin and kernel methods
in the machine learning community.
An offshoot of this framework is that it has enabled us to provide solutions to ill-understood
concepts of active learning from an analytical perspective.
[For Related Publications,
check
here] |
 
Some example kernels
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Another area of my research is centered around the ability to learn incrementally,
in particular, to perform online function approximation in
real time from many sensory channels. As learning in real time with
high dimensional systems often imposes different constraints than those that typical
machine learning algorithms address, I have engaged in the development
of learning mechanisms that are specifically targeted at real-time
learning.
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(2) Efficient Real-Time Incremental Learning using Nonparametric Methods
Adopting a theoretical framework of non-parametric statistics, we have developed
a novel statistical tool - Locally Weighted Projection Regression
(LWPR) for incremental learning in high dimensional systems.
LWPR performs nonlinear function approximation in high dimensional
spaces in the presence of redundant and irrelevant input dimensions.
[For related publications, check
here]
Dimensionality reduction techniques that employ very few projection directions
in spite of large input dimensionality enable the algorithm to scale well to high dimensional problems.
At its core, it uses locally linear models, spanned by a small number
of univariate regressions in selected directions in input space.
A locally weighted variant of Partial Least Squares (PLS) is employed
for doing the dimensionality reduction. This non -parametric
local learning system-- i) learns rapidly with second order learning methods based
on incremental training, ii) uses statistically sound stochastic cross
validation to learn iii) adjusts its weighting kernels based on local information only,
iv) has a computational complexity that is linear in the number of inputs,
and v) can deal with a large number of - possibly redundant - inputs,
as shown in evaluations with up to 50 dimensional data sets.
To our knowledge, this is the first truly incremental spatially localized
learning method to combine all these properties. [For publications exclusively on Dimensionality Reduction techniques, click here] |
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3) Online Statistical Learning for High Dimensional Movement Systems
Implementations
on several robotic hardware at USC, the ATR laboratories and
RIKEN BSI Institute, Japan including a 30 DOF humanoid robot have
demonstrated the potential of this approach – it has been feasible
for the first time to learn dynamics models for such high dimensional
systems incrementally in real time. This scalability has enabled us
to apply our learning framework for socially relevant projects on
learning control for human augmentation (wearable robots),
rehabilitation and complete autonomous learning systems for human-machine
interaction. I believe that this research will have a significant impact
on many other forms of real-time learning tasks including online planning,
process control or adaptive guidance systems in unmanned vehicles.
Some video clips of
the online learning (with LWPR) in action:
1. Learning the camera transformation (2Dx2cameras->3D space) [avi (4.53MB)] (LWPR is used to learn the functional transformation between the 2 camera coordinates (x-y) to 3D coordinates) 2. 7DOF robot arm control using PD control only - no feedforward model [avi(3.28MB)] (This clip shows control using perfect kinematics but no inverse dynamics model - see the lag due to the lack of feedforward model) 3. Online learning of inverse dynamics (compare to PD control only) [avi(14.2MB)] Learning of the dynamics occurs in real time using LWPR- look at the improved control after switching on the online learning) 4. Learning to reach and balance a pole with the dexterous arm [avi (5.39MB)], pole balancing learn sequence [avi (8.15MB)] and online adaptation of changed tool dynamics [avi (6.49MB)] 5. Online inverse dynamics learning in 30DOF Humanoid robot [ avi(11.6MB)]This shows the scalability of the learning to 30DOF robot - the algorithm adapts in real time to the changed dynamics) |
7DOF SARCOS
Dexterous Arm
30DOF Humanoid Robot (DB)
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| (4) Visuomotor Learning
and Multimodal Attention
The work
on learning in high dimensional sensory space and motor control
has led to a natural extension into using these techniques in the area
of multimodal sensor fusion and interaction. I am currently heading
a project on Multimodal Interaction using prototypic robotic vision
head hardware at the RIKEN Brain Science Institute in collaboration
with researchers at USC (look below for details). The aim of this
work is to look at the sensory and motor paths as a strongly coupled
system and to try to reproduce various oculomotor behaviors like VOR,OKR
, smooth pursuit and other sensory (audio, gyroscopic etc.) driven responses
on robotic hardware based on biologically plausible computations. One
of the direct goals of this research is to understand how multimodal sensation
guides the generation of coordinated action and how action guides the
perception of the environment. However, I hope that research will shed
light on the more general principles of information processing in sensor-rich
environments.
Visual
Flow
based Attention involves 3 main modules: (i) Sensory processing
of input modalities based on neo-cortical interaction
dynamics and saliency maps to determine attention locations of
interest (ii) Motor plan for execution of the
overt attention and (iii) Module which maintains the coordinate
updates
[For
related publications, check
here
]
Some video clips on Visual Flow based Attention : 1. Vision Head System - Peripheral and Foveal Cameras [ avi (4.3MB)] 2. Visual Flow Computation [ avi (2.51MB)] 3. Attending to Motion based on visual flow [ avi (21.4MB)] 4. Inhibition of return allows attending to multiple sensory signals[ avi(3.67MB)] 5. DB attending to Natural Motion [ avi (3.74MB)] |
7DOF DB Vision Head with 4 cameras
Cameras - Peripheral and Foveal
Vision
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MAVERic is a versatile
robotic vision head developed for oculo-motor r
 esearch at
RIKEN .
It has 7 DOF and is controlled using
a real time operating system (vxWorks). MAVERic is equipped
with multiple sensory modalities including position sensing
(7DOFs), load sensing (3DOFs), stereo microphones, foveal and
peripheral vision in each eye and a 6-axes gyroscope in addition
to laser range finders in each eye. For details on the Vision Head project in RIKEN, check out the dedicated page |
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(5) Sparse Function Approximation and Coding
To understand
information processing and computations in biological systems,
I strongly believe that in addition to elucidating the pathways
and connections at various scales, it is also essential to look
at the underlying computational principles. I have worked
(to a limited extent) on the principles of sparse representation
for decomposition of natural images and neural codes as well as methods
of minimizing the effect of noise variance in learning systems that
have unreliable and stochastic noisy properties – not unlike our own
neural system.
[For
related publications, check
here
]
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| (6) Bayesian Nonparametric
Learning
One of the
recent thrusts has been to come up with parameter free learning
- in other words, getting rid of the learning rate parameters
of gradient descent approaches and the various initialization issues
in the Max. Likelihood framework while maintaining the advantages
of the local nonparametric techniques. Bayesian Nonparametric learning
is a step towards these goals -- an approach where putting hyperparameters
on the adaptive distance metric allows to automatically determine
the optimum locality. This work will provide a much-needed bridge
between theoretically sound Bayesian learning methods and the highly
adaptive and efficient non-parametric learning techniques.
[For
related publications, check
here
]
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Automatic Adjustment of the Kernel
Distance Metric
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| (7)
Miscellaneous
In addition to
topics mentioned above, I am also interested (to
a varying degree) in some additional topics
listed below. See also the interesting video
clips from associated research.
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