CSCI598A: Robot Intelligence
Apr. 23, 2015
Reasoning Over Time
• Object recognition (static problem)
• We consider spatial relations with uncertainty
• We don’t care about time
• Motion planning (dynamic problem)
• It is a dynamic problem with uncertainty
• Variable values change over time
• Locations, velocity, acceleration of joints
• Time must be modeled to estimate present status and
probably predict future states
• High-Level Task Abstraction (static problem)
• We consider temporal relations of subtasks
2
Issues in Time Reasoning
• Temporal segmentation of streaming/times series
data
• Alignment of time series data
• Reasoning high-level task abstraction
Temporal Segmentation
• A naïve, uniform segmentation
Temporal Segmentation
• A naïve, uniform segmentation
Right: the standard deviation of the scores and its mean computed on a
sliding window. The local minima of the standard deviation function are
break points.
Temporal Segmentation
• A naïve, uniform segmentation
After normalization: Blue dots are the break points computed that
indicate the end of a segmentation and the beginning of a new one.
Temporal Segmentation
• Fuzzy segmentation
Ground truth
The uniform segmentation and many others assume
break points and segments can be distinctly separated.
Temporal Segmentation
• Fuzzy segmentation
Ground truth
Frames
Key Concept:
Gradual Transition
Write on Board
Gradual Transition
Answer Phone
Temporal Segmentation
• Fuzzy segmentation
Ground truth
Proposed
approach
Frames
The fuzzy approach models each segment/event as a
fuzzy set with fuzzy boundaries.
Alignment of Time Series
• Dynamic Time Warping (DTW)
Reasoning Task Abstraction
Goal: reason about chronological order of subtasks
Reasoning Task Abstraction
Goal: reason about chronological order of subtasks
Reasoning Task Abstraction
Reasoning Task Abstraction
Reasoning Task Abstraction
Reasoning Task Abstraction
Reasoning Task Abstraction
Inference tasks:
Reasoning Task Abstraction
Hidden Markov Model:
Reasoning Task Abstraction
Hidden Conditional Random Field (HCRF):
•
•
Learn a mapping from temporal data to a label
Use latent variable to model underlying temporal structures
𝑦
Frame 1
ℎ1
ℎ2
ℎ3
ℎ4
ℎ5
𝒙1
𝒙2
𝒙3
𝒙4
𝒙5
Frame 2
Frame 3
Frame 4
Frame 5
Reasoning Task Abstraction
Example of HCRF
𝑦 = tennis-serve
ℎ ∈ {Toss, Swing, Hit}
𝒉
𝒙
Assuming identical
HCRFs
𝒉: T T T S S S S S H H H H
Summary of the class
• Definition of robot and its intelligence
• Robot perception (perception)
• Sensing technologies
• Object recognition from 2D and 3D
• Learning from demonstration (action)
• Reinforcement learning
• Data and time modeling (reasoning)
• Tutorial of ROS, PCL, and deep learning
• A focus on the Amazon Picking Challenge
Summary of the class
• Definition of robot and its intelligence
• Robot perception (perception)
• Sensing technologies
• Object recognition from 2D and 3D
• Learning from demonstration (action)
• Reinforcement learning
• Data and time modeling (reasoning)
• Tutorial of ROS, PCL, and deep learning
• A focus on the Amazon Picking Challenge
Summary of the class
• A working definition of robot:
Physical machine that generates “intelligent”
connection between perception and action
• Robot intelligence:
Robot intelligence includes recognizing patterns,
comprehending ideas, plan, making decisions, and
communicating
Summary of the class
• Definition of robot and its intelligence
• Robot perception (perception)
• Sensing technologies
• Object recognition from 2D and 3D
• Learning from demonstration (action)
• Reinforcement learning
• Data and time modeling (reasoning)
• Tutorial of ROS, PCL, and deep learning
• A focus on the Amazon Picking Challenge
Summary of the class
• Robot perception
Face examples
Classification
Result
Off-line
training
Classifier
Feature Extraction
Representation
Non-face examples
Search for faces at
different resolutions
and locations
25
Summary of the class
• Bag of word models
1.
2.
3.
4.
Feature detection
Feature description
Dictionary learning
Bag-of-features representation
Summary of the class
• 3D object recognition
Summary of the class
• Definition of robot and its intelligence
• Robot perception (perception)
• Sensing technologies
• Object recognition from 2D and 3D
• Learning from demonstration (action)
• Reinforcement learning
• Data and time modeling (reasoning)
• Tutorial of ROS, PCL, and deep learning
• A focus on the Amazon Picking Challenge
Summary of the class
• Learning from Demonstration
• Learning by watching: correspondence problem
• Learning by acting
• Gaussian mixture models and regressions
Summary of the class
• Learning from Demonstration
• Learning by watching: correspondence problem
• Learning by acting
• Gaussian mixture models and regressions
• Key issues in Learning from Demonstration
• Parameter learning: Expectation-Maximization
• Gaussian component estimation: Bayesian Information
Criteria (BIC)
• Trajectory alignment: Dynamic Time Warping (DTW)
• Dimension reduction: Principal Component Analysis
(PCA)
Summary of the class
• Reinforcement learning
• A learning approach that can adapt through interaction
with the environment
Summary of the class
• Definition of robot and its intelligence
• Robot perception (perception)
• Sensing technologies
• Object recognition from 2D and 3D
• Learning from demonstration (action)
• Reinforcement learning
• Data and time modeling (reasoning)
• Tutorial of ROS, PCL, and deep learning
• A focus on the Amazon Picking Challenge
Summary of the class
• Learning from Data
• Supervised learning
• Unsupervised learning
• K-means
Summary of the class
• Time modeling
• Temporal segmentation
• Sequence alignment
• Reasoning time orders of subtasks
Summary of the class
• Definition of robot and its intelligence
• Robot perception (perception)
• Sensing technologies
• Object recognition from 2D and 3D
• Learning from demonstration (action)
• Reinforcement learning
• Data and time modeling (reasoning)
• Tutorial of ROS, PCL, and deep learning
• A focus on the Amazon Picking Challenge
Summary of the class
• Deep learning
Summary of the class
• Definition of robot and its intelligence
• Robot perception (perception)
• Sensing technologies
• Object recognition from 2D and 3D
• Learning from demonstration (action)
• Reinforcement learning
• Data and time modeling (reasoning)
• Tutorial of ROS, PCL, and deep learning
• A focus on the Amazon Picking Challenge
Summary of the class
• Amazon Picking Challenge
using the Baxter robot
(named Zuko, the firelord)
Summary of the class
• Definition of robot and its intelligence
• Robot perception (perception)
• Sensing technologies
• Object recognition from 2D and 3D
• Learning from demonstration (action)
• Reinforcement learning
• Data and time modeling (reasoning)
• Tutorial of ROS, PCL, and deep learning
• A focus on the Amazon Picking Challenge
Examples of LfD and RL
Work from Dr. Aude Billard
40
Examples of LfD and RL
Work from Dr. Aude Billard
41
Examples of LfD and RL
Work from Dr. Aude Billard
42
Additional Examples
Work from Dr. Aude Billard
43