Once confined to the manufacturing floor, robots are quickly entering the public space at multiple levels: drones, surgical robots, service robots, and self-driving cars are becoming tangible technologies impacting the human experience. Our goal in this class is to learn about and design algorithms that enable robots to reason about their actions, interact with one another, the humans, and the environment they live in, as well as plan safe strategies that humans can trust and rely on.
This is a project-based graduate course that studies algorithms in formal methods, control theory, and robotics, which can improve the state-of-the-art human-robot systems. We focus on designing new algorithms for enhancing safe and interactive autonomy.
The course is a combination of lecture and reading sessions. The lectures discuss the fundamentals of motion planning, formal methods applied in robotics, learning from demonstration, intent inference, and shared control required for modeling and design of safe and interactive autonomy for human-robot systems. During the reading sessions, students present and discuss recent contributions in this area. Throughout the semester, each student works on a related research project that they present at the end of the semester.
There are no official prerequisites, but an introductory course in artificial intelligence and robotics is recommended.
At the end of this course you will have gained knowledge about applications of various topics in designing safe and interactive autonomous systems: temporal logics, reactive synthesis, planning and control, learning and human modeling, game theoretical foundations of interactive systems, safe learning, etc.
You will also have hands-on experience working on a research project and it is expected that you will gain the following research skills: analyzing literature related to a particular topic, critiquing papers, and presentation of research ideas.
|Date||Lecture||Handouts / Deadlines||Notes|
|Tue, Sep 26||Introduction to Safe and Interactive Robotics||Syllabus||
Template for Scribes
|Thu, Sep 28||Lecture: Motion Planning||
|Tue, Oct 03||Lecture: Trajectory Optimization||Mingyu, Maxime, Keven
|Thu, Oct 05||Lecture: Formal Methods in Robotics||
||Eli, Hesam, Kyle, Chelsea
|Tue, Oct 10||Lecture: Formal Methods in Robotics||
|Thu, Oct 12||Reading: Safe Learning and Control||
||Sumeet, Hesam||Tue, Oct 17||Reading: Safe Learning and Control||
P3: Safe Visual Navigation via Deep Learning and Novelty Detection. Richter, et al. (Pros: Mingyu, Cons: Maxime).
|Mingyu, Lin, Maxime, Hans|
|Thu, Oct 19||Reading: Adversarial Neural Networks||
P2: Explaining and Harnesssing Adversarial Examples. Goodfellow, et al. (Pros: -, Cons: -).
P3: Understanding Neural Networks Through Deep Visualization. Yosinski, et al. (Pros: Pengda, Cons: Shane).
|Nipun, Kyle, Shane, Pengda|
|Tue, Oct 24||Reading: Models of Cognition||Project Proposal Reports Due
||Sharon, Chelsea, Yuhang, Pengda|
|Thu, Oct 26||Project Proposal Presentations|
|Tue, Oct 31||Project Proposal Presentations|
|Thu, Nov 02||Project Proposal Presentations|
|Tue, Nov 07||Lecture: Learning from Demonstration||
|Thu, Nov 09||Reading: Learning from Demonstration||
||Karen, Sumeet, Hans, Sharon|
|Tue, Nov 14||Guest Lecture: Mo Chen||Project Milestone Reviews Due|
|Thu, Nov 16||Reading: Intent Inference||
||Yuhang, Gleb, Gene, Haruki|
|Tue, Nov 21||Thanksgiving Break|
|Thu, Nov 23||Thanksgiving Break|
|Tue, Nov 28||Reading: Communication and Coordination||
|Thu, Nov 30||Reading: Collaboration||
||Shane, Nipun, Mingyu, Kyle|
|Tue, Dec 05||Project Presentations|
|Thu, Dec 07||Project Presentations|
|Tue, Dec 12||Project Reports||deadline at midnight (firm)|
|Component||Contribution to Grade|
|Student Presentations & Paper Reviews||30%|
|Scribing & Class Participation||20%|
|Component||Contribution to Grade|
|Project Proposal Reports||5%|
|Project Proposal Presentations||5%|
|Project Milestone Reviews||10%|
|Project Presentation (possibly with demos)||15%|
|Final Project Report||15%|
© Dorsa Sadigh 2017