This is a class about applying autonomy to real-world systems. The overarching theme uniting the many different topics in this course will center around programming a cognitive robotic. This class takes the approach of introducing new reasoning techniques and ideas incrementally. We start with the current paradigm of programming you're likely familiar with, and evolve it over the semester—continually adding in new features and reasoning capabilities—ending with a robust, intelligent system. These techniques and topics will include algorithms for allowing a robot to: Monitor itself for potential problems (both observable and hidden), scheduling tasks in time, coming up with novel plans to achieve desired goals over time, dealing with the continuous world, collaborating with other (autonomous) agents, dealing with risk, and more.
Overview
Syllabus
- Cognitive Robotics: Markov Decision Processes
- Cognitive Robotics: Multi-Agent Planning
- Cognitive Robotics: Planning with Casual Graphs
- Cognitive Robotics: Planning with Temporal Land Marks
- Cognitive Robotics: Programs on State and Planning as Heuristic Forward Search
- Cognitive Robotics: Programs that Execute with Humans
- Cognitive Robotics: Programs that Monitor Hidden State
- Cognitive Robotics: Programs with Flexible Time
- Cognitive Robotics: Risk-Bounded Programming On Continuous State I
- Cognitive Robotics: Robustness Through Model-Based Programming
- Cognitive Robotics: Time-Line Planning Using Casual Graphs
- Cognitive Robotics: Advanced Lecture 1
- Cognitive Robotics: Advanced Lecture 2
- Cognitive Robotics: Advanced Lecture 3
- Cognitive Robotics: Advanced Lecture 4
- Cognitive Robotics: Advanced Lecture 5
- Cognitive Robotics: Advanced Lecture 6
- Cognitive Robotics: Advanced Lecture 7
Taught by
Prof. Brian Charles Williams