Probabilistic Robotics and Models of Gaze...

transcript

Dr. José Ignacio Núñez Varela jose.nunez@uaslp.mx

MICCS 2015

Probabilistic Robotics and

Models of Gaze Control

Part I:

Probabilistic Robotics

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Why do we need robots?

Imagen: http://upload.wikimedia.org/wikipedia/commons/5/5e/KUKA_Industrial_Robots_IR.jpg

Imagen: http://inapcache.boston.com/universal/site_graphics/blogs/bigpicture/robot_08_12/r15_19549703.jpg

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Imagen: http://www.guidedsolutions.co.uk/news/de/wp-content/uploads/2014/06/Robotic-surgery1.jpg

Imagen: http://upload.wikimedia.org/wikipedia/commons/6/64/US_Navy_090512-N-2013O-013_A_Mark_II_Talon_robot.jpg

Imagen: https://www.nasa.gov/images/content/664141main_iss031e148737_full.jpg

But the kind of robot we

really want is …

Imagen: http://img4.wikia.nocookie.net/__cb20100924163215/bigbangtheory/images/e/ed/The-Big-Bang-Theory-The-Robotic-

Manipulation.jpg

No, not really.

What about this …

Imagen: http://www.jsk.t.u-tokyo.ac.jp/research/irt/images/ar-robot.jpg

Imagen: http://inapcache.boston.com/universal/site_graphics/blogs/bigpicture/robot_08_12/r12_19311581.jpg

Unfortunately, there are

still many things to

solve first …

Basic model of

robot interaction

Picture credits: http://www.3ders.org/images/PrimeSense_apple-3d-sensor-1.jpg

http://cdn.shopify.com/s/files/1/0130/8982/products/midi-cpu-large_1024x1024.jpg

Sensing Planning

Acting

Asimo © Honda

We need intelligent

robots!

Intelligent robots

Learning

Reasoning

Decision-making

Planning

Understanding

Common sense

PR2 © Willow Garage

Robots have to be able to accomodate the

enormous uncertainty that exists in the physical

world.

Imagen: http://www.grumpygratefulmom.com/wp-content/uploads/2011/11/messy-kitchen-1024x769.jpg

But, what is

uncertainty?

Being not certain about

something.

Imagen: http://www.allonrobots.com/images/robot-question.jpg

The estimated percentage by which

an estimated or calculated value

may differ from the true value.

Imagen: http://www.threadbombing.com/data/media/30/motorcycle_jump_failure.jpg

Let’s see an

example

grasping

The robot might not have a good

estimate of where the object is

What factors contribute to

the robot's uncertainty?

Robot Environments

Well structured environment

<< uncertainty Not structured environment

>>> uncertainty

Imagen: http://upload.wikimedia.org/wikipedia/commons/5/5e/KUKA_Industrial_Robots_IR.jpg

http://www.archimuse.com/mw2001/papers/giannoulis/giannoulis_fig1.jpg

Sensors are limited in what they can perceive (e.g.,

physical limitations affect range and resolution)

Sensors are subject to noise

Sensors can break

Imagen: http://www.longrangecamera.com/fov.gif

Robot Sensors

Motors are, at some extent, unpredictable

Control noise, wear-and-tear, mechanical failure

Imagen: https://content.solarbotics.com/products/photos/8ddc7ea32073c4756a4cbdaedcbda0fa/lrg/IMG_0681.jpg

Robot Actuators

All internal models of the world are approximate

Model errors have often being ignored

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Robot’s Internal Models

Robots are real time systems, thus limiting the

amount of computation being carried out

Algorithms need to be approximated

Imagen: http://www.pirobot.org/blog/0015/map-1b.png

Algorithmic Approximations

Robots are forced to act even

though they don't have sufficient

information to make decisions with

absolute certainty.

Imagen: http://i.dailymail.co.uk/i/pix/2014/03/11/video-undefined-1C33F90E00000578-54_637x365.jpg

“Managing uncertainty is

possibly the most important

step towards robust real-world

robot systems.”

- Thrun, Burgard and Fox

Probabilistic Robotics

Key idea: Represent uncertainty explicitly

using the calculus of probability theory.

Instead of relying on a single “best guess”,

probabilistic algorithms represent information

by probability distributions over a whole space

of guesses.

Mobile Robot Localization

Imagen: http://clearpath.wpengine.netdna-cdn.com/wp-content/uploads/2013/01/Gallery_TurtleBot_Office.jpg

The map is given

The robot wants to know where it is

The robot assumes a uniform probability distribution

of where it is

(it is likely to be in any place in the map)

The robot’s belief increases after sensing a door

(data is integrated into the old belief)

The robot moves some distance

(Its belief moves as well, but the movement

introduces some noise)

The robot senses a door once again, and its belief of

where it is increases

Bayes Theorem

Imagen: http://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif

Bayes Theorem

Prior probability

Bayes Theorem

Posterior

probability

Part II:

Gaze Control

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Biological

perspective

Gaze Control

Machine

perspective © Jason Babcock © icub.org

Why study gaze control?

Foveal Vision

Eye Movements

Saccades

• Rapid jump-like movements (900°/sec)

• Ballistic (trajectory cannot change)

• Stereotyped (follow the same pattern)

• Voluntary and involuntary

• Aim: Shift the fovea to obtain high resolution samples

Saccade Sequence

We perform hundreds

or even thousands of

saccades every day!

How does the brain

decide where to

fixate next?

Active Vision

Task and context

determine where to

fixate next

Vision and Action

Uncertainty Reduction

Engineering science goal

What mechanisms a rational decision maker

could employ to select a gaze location

optimally, or near optimally, given limited

information and limited computation time

during the performance of a task?

Human behavioural goal

How humans select the next gaze location?

Gaze Control Processes

iCub Humanoid Robot

Two problems

where to look gaze allocation

Pick & Place Task

Models of Gaze Control

• Based on uncertainty reduction (Uncertainty)

• Based on rewards and uncertainty (Rew+Unc)

• Based on rewards, uncertainty and gain (Rew+Unc+Gain)

“What would happen if I look at entity ei?”

One-step look ahead gaze control

Uncertainty Reduction

“How much uncertainty is

reduced if I look at entity ei?”

Reward and Uncertainty

“How much value am I

expected to get after looking

at entity ei?”

Reward, Uncertainty & Gain

“Which motor system would

get more benefit if gaze is

allocated to it?”

Conclusions

•We need robots!

•There is still much to do before we can

buy our assistant robot

•You can contribute to make this

happen!

Thank You!!

E-mail: jose.nunez@uaslp.mx

Website: http://ciep.ing.uaslp.mx/jnunez

Reach/Grasp Sensitivity

Observation Noise

Field of View

Probabilistic Robotics and Models of Gaze...

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