Chapter 21
Robotic
Perception and action
323-670 Artificial Intelligence
ดร.วิภาดา เวทย์ ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์
Robotic
1) Robotics is the intelligent connection of perception action.
2) A robotic is anything that is surprisingly (moving target) animate.
3) perceptual (S/W) + motor task (H/W) [action]
 operate in the real world : searching and backtracking can be costly
 we need operating in a simulate world with full information for an
optimal plan by best-first search
 we can checked preconditions of the operators using perception to
perform action
 real time search : p. 562 A* algorithm, RTA* (Korf 1988)
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Vision
 2D  3D
 signal processing : enhance the image
 measurement analysis : for image containing a single object
determining the 2D extent of the object depicted
 pattern recognition : for single object images, classify the object
into category
 image understanding : for image containing many objects in the
image, classify them, build 3D model of the scene. see Figure 14.8
p. 367
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Vision

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Problem :
Robotic
ambiguous image : see Figure 21.2 p. 564
Figure 21.3 p. 565 using low level knowledge to interpret an image
image factor, sensor fusion : color, reflectance, shading
Figure 21.4 p. 565 using high level knowledge to interpret an image
(a) use surroundings objects to help (b) baseball, log in a fireplace,
amoeba, [egg, bacon, and plate]
 Figure 21.5 p. 567 Image understanding
 analog signal
Image
2D features
3D features
composite
objects
Object
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Speech Recognition

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
speaker dependence (we can train the system) / speaker independence
continuous / isolated word speech
real time SPHINX (1988) / offline processing
large (difficult) / small vocabulary
broad (difficult) / narrow grammar: TANGORA (1985) 20000 words
vocabulary
 HMM (Hidden Markov Modeling) SPHINX system
– statistical learning method
– HMM is a collection of states and transitions
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Speech Recognition
 HMM (Hidden Markov Modeling) SPHINX system
– statistical learning method
– HMM is a collection of states and transitions
– each transition learning a state is marked with
1) the probability which that transition is taken
2) an output symbol
3) the probability that the output symbol is emitted when the transition
is taken.
– the problem of decoding a speech waveform turns into the
problem of finding the most likely path (set of transitions) through
an323-670
appropriate
ArtificialKMM.
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Action



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
p. 569 : navigation around the world
planning routes / path planning
reaching desired destinations without bumping into things
see Figure 21.6–21.9 p. 570-571
constructing a visibility graph
 configuration space / C-space (Lozano-Perez 1984)
– basic idea is to reduce the robot to a point P and do path planning
in an artificially constructed space
– rotation (X,Y,)
 obstacles can be transformed into 3D C-space objects, visibility graph
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can be 323-670
createdArtificial
and searched.
Manipulation
 end-effectors (two-gripper) / a human like hand
 pick-and-place : grasp and object and move it to a
specific location see Figure 21.10-21.11 p. 572-573
 Figure 21.11-21.12 (a) naive strategy for grasping and placement
 Figure 21.11-21.12 (b) clever strategy for grasping and placement
 planning p. 332 e.g. Block world ON(A,B) HOLDING ,
ARMEMPTY
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Manipulation
 planning p. 332 e.g. Block world ON(A,B)
HOLDING , ARMEMPTY
 Components of a planning system
1) choose the best rule to apply
2) applying rules see Figure 13.2-13.3 p. 336-337
3) detecting a solution
4) detecting dead ends
5) repairing an almost correct solution
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Chapter 22
Conclusion
323-670 Artificial Intelligence
ดร.วิภาดา เวทย์ ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์
Components of AI program
 p. 579
 1) Methods for representing and using knowledge
 2) Methods for conducting heuristic search
 both methods relate to each other
 Knowledge Representation: use to solve the problem
1) Predicate Logic : use to solve a new derive inference problem
2) Semantic Networks : use for network search routines
3) Set of weight in NN : some relaxation or forward propagation
search must be exploited.
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Knowledge
1) “Essential Knowledge”
: knowledge about defining what problem to be solved, how
to solve the problem, and what is the outcome or solution
of the problem solving.
2) “Heuristic Knowledge”
: knowledge about the explanation of how to get the
outcome or solution.
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AI
Problem
and search
Knowledge
Representatio
n
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AI Fields
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problem and search
AI
technique
Production
system
Heuristic
search
Generate
and test
best first
search
constrain
satisfaction
mean-end
analysis
hillclimbing
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Knowledge Representation
predicate
logic
rule
semantic
network
frame
conceptual
dependenc
y
statistical
model *
resolution
forward/
backward
chaining
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uncertainty *
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AI Fields
Robotic
planning
learning
Expert
system
NLP
Computer
Vision
common
sense *
NN
block world
pattern
recognition
understanding
conceptual
dependency
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rule
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Heuristic
search
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