CS 333: Safe and Interactive Robotics

Fall 2018-2019, Class: Tue, Thu 1:30-2:50pm, School of Education 334


As the field of robotics is quickly emerging, one critical and challenging subject is ensuring that robotic systems can work safely with humans. This course covers a diverse set of topics that focus on addressing the most critical aspects of building interactive and safe autonomous systems. Students will practice essential research skills including critiquing papers, debating, reviewing, writing project proposals, and presenting ideas effectively.


The course is a combination of lecture and reading sessions. The lectures discuss the fundamentals of topics 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. See detailed course policies.


Introductory courses in artificial intelligence and robotics are recommended, but not required.

Learning Objectives:

At the end of this course you will have gained knowledge about applications of various topics in designing safe and interactive autonomous systems.

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.


Dorsa Sadigh

Dorsa Sadigh


Office Hours: Thu 3-4PM
Location: Gates 142
Jerry He

Jerry He

Course Assistant

Office Hours: Tue 3-5PM
Location: Gates 120


Date Lecture Handouts / Deadlines Notes
Week 1
Tue, Sep 25
Lecture Introduction to Safe and Interactive Robotics Sample Long Review
Week 1
Thu, Sep 27
Lecture Motion Planning
  • Spatial Planning: A Configuration Space Approach. Lozano-Perez. (1983).
  • Analysis of Probabilistic Roadmaps for Path Planning. Kavraki, et al. (1988).
  • Randomized Kinodynamic Planning. LaValle, et al. (2001).
  • Path Planning in Expansive Configuration Spaces. Hsu, et al. (1997).
Week 2
Tue, Oct 02
Lecture Trajectory Optimization
Week 2
Thu, Oct 04
Lecture Optimal Control and Reinforcement Learning
Week 3
Tue, Oct 9
Reading Task and Motion Planning P1 Pros: Erdem
P1 Cons: Shushman
P2 Pros: Shushman
P2 Cons: Erdem
Week 3
Thu, Oct 11
Lecture Learning from Demonstration
  • Maximum Margin Planning. Ratliff, et al. (2006).
  • Maximum Entropy IRL. Ziebart, et al. (2010).
  • Movement Primitives via Optimization. Dragan, et al. (2015).
  • Active Preference-Based Learning of Reward Functions. Sadigh, et al.(2017).
Week 4
Tue, Oct 16
Lecture Learning from Demonstration
  • Socially Compliant Mobile Robot Navigation via IRL. Kretzschmar, et al. (2016).
  • Predicting Human Reaching Motion in Collaborative Tasks using Inverse Optimal Control and Iterative re-planning. Mainprice, et al. (2015).
  • Infant Imitation After a 1-Week Delay. Meltzoff, et al. (1988).
  • Planning Based Prediction for Pedestrians. Ziebart, et al. (2009).
Week 4
Thu, Oct 18
Reading LfD + Preference based Learning Due Project Proposal Reports
P1 Pros: David
P1 Cons: Minae
P2 Pros: Nick
P2 Cons: Vidush
Week 5
Tue, Oct 23
Presentation Project Proposal Presentations
Week 5
Thu, Oct 25
Reading Safe Learning P1 Pros: Anand
P1 Cons: Erdem
P2 Pros: Mengxi
P2 Cons: Xieyuan
Week 6
Tue, Oct 30
Lecture Guest Lecture
Bradford Neuman, Anki
  • Creating Interactive Robots With Character
Week 6
Thu, Nov 01
Lecture Guest Lecture
Roberto Martin-Martin, Stanford
  • JackRabbot: Social Navigation and Interaction in Human Environments
Week 7
Tue, Nov 06
Reading Intent Inference P1: Masha
P2: Nikhil
P3: Qizhan
P4: Zheqing
Week 7
Thu, Nov 08
Reading Shared Control P1: Minae
P2: Ashley
P3: Arthur
P4: Zixuan
Week 8
Tue, Nov 13
Reading Communication and Coordination P1 Pros: Kyle
P1 Cons: Nick
P2 Pros: Andy
P2 Cons: Mengxi
Week 8
Thu, Nov 15
Reading Collaboration Due Project Milestone Reviews Due P1 Pros: Haoze
P1 Cons: Peter
P2 Pros: Sean
P2 Cons: David
Week 9
Tue, Nov 20
Thanksgiving Break
Week 9 Thu, Nov 22 Thanksgiving Break
Week 10
Tue, Nov 27
Lecture Formal Methods in Robotics
  • Church’s Problem Revisited. Kupferman and Vardi. (1999).
  • Temporal Logic-based Reactive Mission and Motion Planning. Kress-Gazit, et al. (2009).
  • A Fully Automated Framework for Control of Linear Systems from Temporal Logic Specifications. Kloetzer, et al. (2008).
  • Synthesis for Human-in-the-Loop Control Systems. Li, et al. (2014).
  • Optimization-based Trajectory Generation with Linear Temporal Logic Specifications. Wolff, et al. (2014).
  • Model Predictive Control with Signal Temporal Logic Specifications. Raman, et al. (2014).
  • Synthesis of Human-in-the-Loop Control Protocols for Autonomous Systems. Feng et al. (2016).
  • Provably Safe and Robust Learning-based Model Predictive Control. Aswani, et al. (2013).
Week 10
Thu, Nov 29
Presentation Project Presentation
Week 11
Tue, Dec 04
Presentation Project Presentation
Week 11
Thu, Dec 06
Lecture Guest Lecture Katherine Driggs-Campbell, Stanford
Week 12
Tue, Dec 11
Project Reports Due Deadline at midnight (Firm)

Grading Metrics

Component Contribution to Grade
Final Project 50%
Student Presentations & Paper Reviews 40%
Pop-quizzes & Class Participation 10%
Total 100%

Project Grading

Component Contribution to Grade
Project Proposal Reports 5%
Project Proposal Presentations 5%
Project Milestone Reviews 10%
Project Presentation (Possibly with Demo) 10%
Final Project Report 20%
Total 50%

Grading Policies

Final Project (50%): Each student is required to work individually or in groups of up to three people on a research project. The project requires a 2-page proposal including the relevant literature survey, a proposal presentation, a 2-page milestone review, a 6-8 page final report in (double-column IEEE format), and a final presentation/demo. All the page limits exclude references. Students who are taking the class for 4 units are required to work individually on their projects.

Student Presentation & Paper Reviews (40%): All students will get a chance to present multiple papers throughout the class during the reading days. Each paper will have two presenters each discussing the pros or cons of the paper. The presenters need to send the reviews (conference style) of their reading assignments by the midnight before the day of the class. The presentation grade is based on how well the material is presented in both the written review and the talk, how well it is connected to the rest of the papers or class, and how prepared the student is in answering questions from the class.

Every other student who will not be presenting is still required to write a short review of the two papers presented in reading days by noon on the day the paper is presented. The short reviews should just be a couple of sentences summarizing each of the two papers.

Pop-quizzes & Class Participation (10%): There are a few short pop quizzes throughout the quarter on lecture material and some of the paper readings. All students should participate in the discussions on each paper during the reading days.

Project Instructions

The research project throughout the class should study a new research problem, i.e., design a new algorithm, study a new application, etc. Literature surveys are not acceptable. The main deliverables of the project are:

Project Proposal Reports (5%): A 2-page proposal that has identified the problem definition, a literature survey on the problem, a potential solution, and a timeline.

Project Proposal Presentation (5%): A short presentation discussing the proposal, limitations, and challenges.

Project Milestone Reviews (10%): A 1-2 page writeup that goes through the progress so far, if there needs to be any changes to the goals, and the updated timeline. You should schedule a meeting with me or the CA to go over the milestone reviews.

Project Presentation, Possibly with Demo (10%): A short (~15-min) presentation reporting the final findings of the project.

Final Project Report (20%): A 6-8 page project report (in double column IEEE format).

This class is partially based on the following existing courses:
Algorithmic Human-Robot Interaction (Berkeley)
Cooperative Machines (MIT)
Computer-Aided Verification (Berkeley)
Human-Robot Interaction (Georgia Tech)

    © Dorsa Sadigh 2018