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Hidden Markov Models CS4758: Robot Learning Ashutosh Saxena Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore.

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Page 1: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Hidden Markov Models

CS4758: Robot Learning

Ashutosh SaxenaAshutosh Saxena

Lecture 14

Lecture slides also taken from Eric

Sudderth and Andrew Moore.

Page 2: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Target Tracking

Radar-based tracking

of multiple targets

Visual tracking of

articulated objects(L. Sigal et. al., 2006)

• Estimate motion of targets in 3D world from indirect, potentially noisy measurements

Page 3: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Robot Navigation: SLAMSimultaneous Localization and Mapping

CAD

LandmarkSLAM

(E. Nebot,Victoria Park)

CADMap

EstimatedMap

• As robot moves, estimate its pose & world geometry

(S. Thrun,San Jose Tech Museum)

Page 4: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Financial Forecasting

• Predict future market behavior from historical data, news reports, expert opinions, …

http://www.steadfastinvestor.com/

Page 5: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Outline

Introduction to Temporal Processes

� Markov chains

� Hidden Markov models

Discrete-State HMMs

� Inference: Filtering, smoothing, Viterbi, classification� Inference: Filtering, smoothing, Viterbi, classification

� Learning: EM algorithm

Continuous-State HMMs

� Linear state space models: Kalman filters

� Nonlinear dynamical systems: Particle filters

Applications and Extensions

Page 6: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 7: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 8: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 9: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 10: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 11: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 12: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 13: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 14: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Example

Page 15: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 16: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
Page 17: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
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Page 29: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of
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Outline

Introduction to Temporal Processes

� Markov chains

� Hidden Markov models

Discrete-State HMMs

� Inference: Filtering, smoothing, Viterbi, classification� Inference: Filtering, smoothing, Viterbi, classification

� Learning: EM algorithm

Continuous-State HMMs

� Linear state space models: Kalman filters

� Nonlinear dynamical systems: Particle filters

Applications and Extensions

Page 86: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Discrete HMMs: Observations

Discrete Observations

Continuous Observations

Page 87: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Linear State Space Models

• States & observations jointly Gaussian:� All marginals & conditionals Gaussian

� Linear transformations remain Gaussian

Page 88: Ashutosh Saxena Lecture 14 - Cornell University...Ashutosh Saxena Lecture 14 Lecture slides also taken from Eric Sudderth and Andrew Moore. Target Tracking Radar-based tracking of

Simple Linear DynamicsBrownian Motion Constant Velocity

Time Time


Jaeyong Sung, Ian Lenz, and Ashutosh Saxenaasaxena/papers/robo... · Jaeyong Sung, Ian Lenz, and Ashutosh Saxena Abstract—A robot operating in a real-world environment needs to

Kavita Lalwani, Pooja Saxena, Kirti Ranjan, Ashutosh Bhardwaj Department of Physics & Astrophysics University of Delhi Manoj Sharan High Energy Nuclear

Robotic Grasping of Novel Objects using Visionang/papers/ijrr08-graspnovel...Robotic Grasping of Novel Objects using Vision Ashutosh Saxena, Justin Driemeyer, Andrew Y. Ng Computer

OBJECT ORIENTED PROGRAMMING LECTURE 12 Instructor: Rashi Garg Coordinator: Gaurav Saxena

Human Centred Object Co-Segmentation arXiv:1606.03774v1 ... · Human Centred Object Co-Segmentation Chenxia Wu 1, Jiemi Zhang2, Ashutosh Saxena , and Silvio Savarese3 1 Computer Science

Lecture 11: Threat Modeling CS 436/636/736 Spring 2012 Nitesh Saxena

Lecture 4.2: Key Distribution CS 436/636/736 Spring 2014 Nitesh Saxena

Beyond Geometric Path Planning: When Context Matters Ashesh Jain, Shikhar Sharma Thorsten Joachims and Ashutosh Saxena

3-D Reconstruction from Sparse Views using … Reconstruction from Sparse Views using Monocular Vision Ashutosh Saxena, Min Sun and Andrew Y. Ng Computer Science …

Performance Analysis of ANN models for the prediction of I ...tmu.ac.in/college-of-computing-sciences-and-it/wp... · 1Naveen Kumar Pandey, 2Prof. Abhay Saxena, 3Dr. Ashutosh Bhatt

Learning preferences for manipulation · Learning preferences for manipulation tasks from online coactive feedback Ashesh Jain, Shikhar Sharma, Thorsten Joachims and Ashutosh Saxena

Learning Haptic Representation for Manipulating … Haptic Representation for Manipulating Deformable Food Objects Mevlana C. Gemici and Ashutosh Saxena Department of Computer Science,

Depth Estimation Using Monocular and Stereo Cues€¦ · Depth Estimation using Monocular and Stereo Cues Ashutosh Saxena, Jamie Schulte and Andrew Y. Ng Computer Science Department

RoboBrain: Large-Scale Knowledge Engine for Robotsasaxena/papers/robobrain2014.pdf · RoboBrain: Large-Scale Knowledge Engine for Robots Ashutosh Saxena, Ashesh Jain, Ozan Sener,

Learning 3-D Scene Structure from a Single Still Imageasaxena/reconstruction3d/saxena_iccv... · 2009-07-23 · Learning 3-D Scene Structure from a Single Still Image Ashutosh Saxena,

3-D Depth Reconstruction from a Single Still Imageasaxena/learningdepth/ijcv... · 2009-07-22 · 3-D Depth Reconstruction from a Single Still Image Ashutosh Saxena, Sung H. Chung,

Jaeyong Sung, Bart Selman and Ashutosh Saxena · 2018-09-10 · Jaeyong Sung, Bart Selman and Ashutosh Saxena Abstract—Many tasks in human environments require per-forming a sequence

Depth Estimation Using Monocular and Stereo Cues › Proceedings › 07 › Papers › 354.pdfDepth Estimation using Monocular and Stereo Cues Ashutosh Saxena, Jamie Schulte and Andrew

High Speed Obstacle Avoidance using Monocular Vision and Reinforcement Learning Jeff Michels Ashutosh Saxena Andrew Y. Ng Stanford University ICML 2005

Make3D: Depth Perception from a Single Still Imageai.stanford.edu/.../Saxena_depthperception_aaai08.pdf · Make3D: Depth Perception from a Single Still Image Ashutosh Saxena, Min

DeepMPC: Learning Latent Nonlinear Dynamics for … Learning Latent Nonlinear Dynamics for Real-Time Predictive Control Ian Lenz 1, Ross Knepper , and Ashutosh Saxena,2 1Department

Robotic Grasping of Novel Objects using Visionai.stanford.edu/...roboticgraspingofnovelobjects.pdf · Robotic Grasping of Novel Objects using Vision Ashutosh Saxena, Justin Driemeyer,

Machine Learning for Protein Classification Ashutosh Saxena CS 374 – Algorithms in Biology Thursday, Nov 16, 2006

Learning to Place New Objects - Stanford University Computer … · 2015-02-28 · Learning to Place New Objects Yun Jiang, Changxi Zheng, Marcus Lim and Ashutosh Saxena Abstract

A vision-based system for grasping novel objects in cluttered environments Ashutosh Saxena, Lawson Wong, Morgan Quigley, Andrew Y. Ng 2007 Learning to

A Green Software Development Life Cycle for Cloud …fac.comtech.depaul.edu/yhwang1/Articles_KHU/article_8… ·  · 2015-03-21Nitin Singh Chauhan and Ashutosh Saxena, Infosys, India

A Microprocessor based Speech Recognizer for …asaxena/papers/speechrecognizer.pdfA Microprocessor based Speech Recognizer for Isolated Hindi Digits Ashutosh Saxena and Abhishek Singh

Lecture 10: Security Design Principles CS 436/636/736 Spring 2012 Nitesh Saxena

Human Centred Object Co-Segmentation arXiv:1606.03774v1 [cs.CV… · 2018. 9. 10. · Human Centred Object Co-Segmentation Chenxia Wu 1, Jiemi Zhang2, Ashutosh Saxena , and Silvio

3-D Depth Reconstruction from a Single Still Imageai.stanford.edu/~ang/papers/ijcv07-monocular3dreconstruction.pdf · 3-D Depth Reconstruction from a Single Still Image Ashutosh Saxena,

Gabriel Yu and Jing Chen (Advised by Ashutosh Saxena)cs229.stanford.edu/proj2007/YuChen-Inferencing3D...Gabriel Yu and Jing Chen (Advised by Ashutosh Saxena) 1. Introduction! In this

3-D Depth Reconstruction from a Single Still Imageasaxena/learningdepth/ijcv_monocular3d... · 3-D Depth Reconstruction from a Single Still Image Ashutosh Saxena, Sung H. Chung, Andrew

ICAR€¦ · and Preservation of Fruits and Vegetables (in 2 Parts Arun S.Mujumdar Mishra, Kumar Bijesh Sharma, H.K. & Ashutosh U adh a Joshi V.K. & Satish Sharma Riar, C.S. & Saxena,

Intro to Mechatronics 18 February 2005 Student Lecture: Transistors Andrew Cannon Shubham Saxena

TECHKRITI LECTURE SERIES Communication BY: Ashutosh Kumar