Improving Quality of Decision-Making in Android Robots Anshuman - - PowerPoint PPT Presentation

Improving Quality of Decision-Making in Android Robots

Anshuman Radhakrishnan, Nikhil Murthy, Jennie Wang

Our Team

• Batteries in Black: FIRST Tech Challenge team since

2007

• Attended the World Championships multiple times
• Received the “Sensor Savant”Judge’s Award at the 2015

World Championship

FIRST and FIRST Tech Challenge

• FIRST: For Inspiration and Recognition of Science and Technology
• FTC (FIRST Tech Challenge): FIRST program for high schoolers that

provides an opportunity for “real world engineering”

• Students design and build a robot and program it using Java-based software

Components of an FTC Robot

• Over the course of the game, there is an Autonomous portion where

the robot needs to operate without human input

• In this portion, more points can be scored if the robot can sense its

environment and make decisions

• Examples of Decisions to be made:
• Choosing between two different paths if one path is blocked
• Stopping the robot before it tips
• Determining the position of a randomly placed object

Decision Making

Challenges in Decision Making and Motivation

• Dynamic environment
• Variations in positions and conditions of game elements
• Motion of other robots
• Decision must be made based off of external factors
• Must be thoroughly tested

Machine Learning

• Used widely in computer science and other fields
• “Gives computers the ability to learn without being

explicitly programmed” through data

• Allows robots to learn from past scenarios and adapt to

new situations

• Data is provided in terms of values for “features” or

variables which are suited to the task

Machine Learning: Context of Robotics

Testing of Algorithms

• When collecting data, we partitioned our data into training and testing

sets- one set is used to develop the model, and the other is used to evaluate the model

• After performing an evaluation of the test set accuracy, the model is

implemented into the main Autonomous routine

• There may also be a cross-validation set used to tune

hyperparameters for an algorithm, but that was not needed here

Classification vs Regression

• Classification involves identifying to which category

something belongs to

• Regression involves estimating the value of some

response variable

• In robotics, we are more concerned with Classification

Survey of Classification Methods

• Logistic Regression
• Fits a Sigmoid Function to Data and returns a Probability
• Support Vector Machines
• Finds the optimal separator between classes
• Naive Bayes
• Assumes feature independence to return a Probability
• Decision Tree Classifiers
• Use Decision Trees to map feature values to classes
• Neural Networks
• Used in Deep Learning

Why Logistic Regression is preferred (for Robotics)

• Easy to interpret (returns a Probability)
• Overfitting can be reduced with Regularization
• Easy to update the model (Online Learning)
• Works better for data that is not cleanly separable

(probability threshold can be adjusted)

Implementation

• scikit-learn (Python)
• caret (R)
• Octave/Matlab

Case Studies

• Case Study 1: Use an IR seeker sensor to determine the

position of an IR beacon from a set of possible positions

• Case Study 2: Use a Color Sensor to determine the color of a

Beacon (Red or Blue)

• Case Study 3: Use internal sensors in an Android Phone to

determine the robot’s orientation, allowing the robot to detect tipping

• Determine which position an IR beacon is placed
• Ambient Lighting Conditions can make detecting the IR

Beacon difficult

• Machine learning allows the robot to determine this position

in different environments

• Collected data of the possible orientations of the beacon
• Using a One vs. All method with three logistic regressions,

the position of the IR beacon can be determined

Case Study 1: IR Seeker

• Determine whether a color is either red or blue

(randomly assigned)

• A machine learning algorithm allows the robot

to classify the color under variable lighting conditions

• Use an RGB sensor that outputs many values
• These are inputs into the machine learning

algorithm

Case Study 2: Color Sensor

Case Study 2: Machine Learning Program

• Four datasets were used for training, four for testing
• Data was averaged over time for each training dataset
• Tested under four different lighting conditions
• Algorithm performed with 100% accuracy on predicting

the right color

• These parameters correspond to each feature (column

vector shown to the left)

Case Study 3: Orientation Determination

• Use internal sensors within the phone
• Collected data from five phones in five different
• rientations
• 60% of the data was used for training
• 40% was used for testing
• Used to detect when the robot is tipping and in what

direction (Multiclass Classification)

Case Study 3: Data

• Sensor data gathered from the internal sensors in each position (1 through 5)

Case Study 3: Confusion Matrix

• Confusion Matrix above shows that the algorithm performed with 100%

accuracy

Future Work

• Using Neural Networks to tune PID controllers for moving

straight and turning

• Reinforcement Learning such that the robot can learn to

perform an Autonomous Program with less explicit programming

• Object Recognition with Deep Learning

Conclusion

• We investigated utilizing machine learning to improve decision-making

in the context of robotics

• The key contributions of this presentation are:
• Developed a process to determine when Machine Learning is

applicable in Robotics

• Identified key algorithms to use and determined a good algorithm

for decision-making in robotics

• Performed several Case Studies in the context of the FIRST Tech

Challenge