| Part A: Robots that Plan and Act in the World |
| 1 |
Introduction to Cognitive Robots:
Remote Explorers and Human Interation Systems (PDF - 1.6 MB) |
| A1: Robots that Deftly Navigate |
| 2 |
Planning Routes by Generating Maps:
Configuration Spaces, Visibility Graphs, Voronai Diagrams, Potential Fields, and Cell Decomposition (PDF) |
| 3 |
Randomized Path Planning:
Kino-dynamic Planning, Planning with Moving Obstacles, Probabilistic Roadmaps (PRMs), Rapidly Exploring Random Trees (RRTs) (PDF) (Courtesy of Stanislav Funiak, Nathan Ickes, and Aisha Walcott. Used with permission.) |
| A2: Planning and Executing Complex Missions |
| 4 |
Path Planning in Unknown Environments:
An Overview (PDF) |
| 5 |
Incremental Path Planning:
Single Source Shortest Path, D*, LRTA* (PDF) |
| 6 |
Mission-level Task Planning:
Partial Order Planning, Constraint-based Interval Planning, and Simple Temporal Networks (STNs) (PDF 1)
"Fast Solutions to CSP’s." Based on PROSSER, P. "Hybrid algorithms for the constraint satisfaction problem." Computational Intelligence 9 (1993): 268-299. (PDF) |
| Part B: Robots that are State-Aware |
| 7 |
Foundations of Estimation:
Bayes Filters, Kalman Filters, and HMMs (PDF) |
| B1: Robots that Find Their Way in the World |
| 8 |
Determining Location Through Particle Filters:
MCMC Methods, Rejection Sampling, Importance Sampling, Metropolis, Particle Filters for Localization (PDF - 1.8 MB) |
| 9 |
Learning Maps:
Scan-matching, ICP, SLAM using Kalman Filters, Topological Maps, Fast-Slam (PDF) |
| B2: Robots that Deduce and Control Their Internal State |
| 10 |
Model-based Programming and Model-based Diagnosis:
Model-based Diagnosis (PDF) |
| 11 |
Conflict-directed Diagnosis and Probabilistic Mode Estimation:
Consistency-based Diagnosis (PDF) |
| 12 |
Incremental Mode Estimation and Hybrid Systems:
Incremental Logical Inference, Trajectory Tracking for Constraint-based, Gaussian Filtering for Hybrid HMMs (K-Best and Rao-Blackwell Particle Filtering) (PDF 1) (PDF 2) (Courtesy of Stanislav Funiak. Used with permission.) |
| 13 |
Optimal CSPs and Conflict-directed A*:
Constraint Satisfaction Problems and Conflict-directed A* Search (PDF) |
| 14 |
Context-based Vision:
Guest Lecture by Bill Freeman (PDF - 2.0 MB) (Courtesy of William Freeman, Kevin Murphy, and Antonio Torralba. Used with permission.) |
| Fast Planning |
| 15 |
Planning as Heuristic Forward Search:
FF Planning (PDF 1) (PDF 2)
Student Advanced Lectures:
LPG: Local Search for Planning Graphs (Seung Chung) (PDF) (Courtesy of Seung Chung. Used with permission) |
| 16 |
Student Advanced Lectures:
Fast Solutions to Constraint Satisfaction Problems (Robert Effinger and Dan Lovell) (PDF 1 - 1.7 MB) (PDF 2) |
| Cooperative Planning |
| 17 |
Student Advanced Lectures:
Distributed CSPs and Task Assignment (Thomas Leaute and Justin Werfel) (PDF) (Courtesy of Thomas Leaute. Used with permission.) |
| 18 |
Student Advanced Lectures:
Distributed Reinforcement Learning and MDPs (Lars Blackmore and Steve Block) |
| Vision-based Exploration |
| 19 |
Student Advanced Lectures:
Vision-based SLAM (Soren Riisgaard) (PDF) (Courtesy of Vikash Mansinghka and Soren Riisgaard. Used with Permission.) |
| 20 |
Student Advanced Lectures:
Information Based Adaptive Robotic Exploration (Morten Rufus Blas) (PDF - 2.6 MB) (Courtesy of Morten Blas. Used with permission.)
Whaite, P., and F. P. Ferrie. "Uncertainty and Visual Exploration." In IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 10. |
| Part C: Robots that Preplan for an Uncertain Future |
| 21 |
Reactive Planning in Large State Spaces Through Decomposition and Serialization (PDF) (Courtesy of Seung Chung. Used with permission.)
Student Advanced Lectures:
SIFT SLAM Vision Details (PDF) (Courtesy of Vikash Mansinghka. Used with permission.) |
| 22 |
The Linear Programming Approach to Approximate Dynamic Programming:
Guest Lecture by Daniela Pucci de Farias
Markov Decision Processes, Approximate Dynamic Programming and Linear Programming, Performance and Error Analysis, and Constraint Sampling |
| 23 |
Partially Observable Markov Decision Processes:
POMDPs, Policy Trees and Value Iteration (PDF) |
| 24 |
Approximate Solutions to POMDPs:
Heuristics, Coastal Navigation, and Real World Apps (PDF) |
| 25 |
Dynamic Scheduling and Execution:
Temporal Plan Execution, Dynamic Scheduling, and Simple Temporal Networks (PDF - 1.0 MB) |
| 26 |
Project Demonstrations:
10 Minute Student Presentations |
