Transcript Clearing a Pile of Unknown Objects using Interactive

Seminar_Robotics
Hao Ding INFOTECH
 Objective
 Main



Overview
Task
Generating Object Hypotheses
Verifying Object Hypotheses
Action Selection And Compliant Interaction
 Summary
and Q&A
Objective: Robot is going to move of
unknown objects in front of him to another
destination.

 First step for large variety of applicatons: household,
space exploration to search and rescue missions.
Title: Clearing a Pile of Unknown Objects using
Interactive Perception

In practice application, the objects is in irregular shape,so
the ability of clearing a pile of unknown objects is
required.

Robtics are required to reveal and verify perceptual
information autonomous, it is so called interactive
perception.
A typical pile of
unknown objects used
in experimetns
For Example: Object on desk
Question: How to move it?
Where
What
How
• Seperating the object from background
• Recognize what is it mostly depending on our experiences
• Select the appropriate action and do it
WE
Where
What
How
• Seperating the
object from
background
• Recognize what
is it mostly
depending on our
experiences
• Select the
appropriate
action and do it
ROBOTS
Where
What
How
• generating
Object
Hypotheses
• Verifying Object
Hypotheses on
self-learning
• Action Selection
And Compliant
Interaction
 Objective
 Main



Overview
Task
Generating Object Hypotheses
Verifying Object Hypotheses
Action Selection And Compliant Interaction
 Summary
and Q&A

RGB:
a color model, in which red, green and blue
are added together in various ways to
reproduce a broad aray of colors.

SURFACE NORMAL: In three-dimension, the surface normal at point P is

Least-Squares: The method of least-squares is a standard approach to
a vector that is perpendicular to the tangent plane
to that surface at P:
the approximate solution of overdetermined systems.
Y= aX+b
If we have x,y with
probability
deistribution , how to
get a and b?
KINECT DEPTH SENSOR (RGB-D SENSOR):




Invented by Microsoft Corp for Xbox 360 gaming
console first.
Low cost.
SDK interface is provided.
Depth and color information at each pixel can be
obtained.
RGB images
Depth images
Step.1: Depth Dicontinuties Computing
Computing the maximal depth change between every pixel
P1
P2
and its immediate 8 neighbors.
P0
P8
P3
P7
P4
P6
P5
If this distance is larger than 2cm, the pixel is marked as a
depth discontinuity, and with different color coded.
Step.2: Surface Normal Computing
Remember previous least-square method?
1. A(x-x0)+B(y-y0)+C(z-z0)=0 plane which include p0
2. z=(A,B,C,x,y)=(-A*x – B*y + A*x0 + B*y0 + C*z0)/C
function of Z
n
Q( A, B, C )  [ A( xi  x 0)  B( yi  y 0)  C ( zi  z 0)]2
i 1
3. We want to get make above covariance minimum
4. So we get A, B and C
Step.3:Facets extracting
1.
Overlaying the depth discontinuities over the surface normals.
2.
Extracting facets = Extracting contiguous colors regions in an
image.
3.
The mean-shift segmentation algorithm implemented here. (no
discuss here as it is implemented by OpenCV)
 Objective
 Main



Overview
Task
generating Object Hypotheses
Verifying Object Hypotheses
Action Selection And Compliant Interaction
 Summary
and Q&A

SIFT(Scale-invariant feature transform): algorithm to detect
features in image.

“Features” of an object in image: e.g, color, size, texture, shape. etc

Function: To identify the object in a test image containing many other
objects, even the object is under changes in image scale, noise and
illumination.
The cat’s ear still
can be recognized
according to SIFT
a) Pushes a facet for 3cm.
b) Object moved, re-compute segmentation.
c) Condition of verified passed:
1.
2.
Object moved.
Segmentation matched previous one.

Facet matching computes the similarity between
two facets by considering a variety of features:
1.
Relative size
2.
Relative area
3.
Average color
4.
Color histogram
5.
SIFT large to small
6.
SIFT large to small valid
7.
SIFT small to large
8.
SIFT small to large valid
 Objective
 Main



Overview
Task
generating Object Hypotheses
Verifying Object Hypotheses
Action Selection And Compliant Interaction
 Summary
and Q&A

The center of gravity:

Principal axis:
Then Robot know where and how to grasp the
object.
Compliancy Definition: a measure of the ability of a
manipulator to react to interaction forces and
torques.(Proposed by H.Kazerooni in 1986)


A video is played here to make good understanding for meaning of compliant action:
http://www.youtube.com/watch?v=u8HejzS8DVI
There are two action defined here:
Compliant poking(pulling/pushing)
Compliant Grasping
 Objective
 Main



Overview
Task
generating Object Hypotheses
Verifying Object Hypotheses
Action Selection And Compliant Interaction
 Summary
and Q&A
1
• Generating
Object
Hypotheses
2
Verifying
Object
Hypotheses on
self-learning
3
Action
Selection And
Compliant
Interaction
Finally, let us watch a vedio for the entire
process:http://www.dubikatz.com/autonomousManipulation.htm
l