Human-Inspired Robotics Lab

Zhi (Jane) Li (

Jun 28th, 2018

RBE 595 – Synergy of Human and Robotic Systems


Zhi Jane Li

Office: 85 Prescott 223C

Email: zli11 [at]

Time & Location: Mon & Wed, 1:00-2:20pm at OH109

Office Hour: Thurs, 3:00-4:00pm at Instructor’s office

TA Office Hour: Mon, 3:00-4:00pm


This course covers topics on (1) the design and motion control of robotic systems that can be directly controlled by human (e.g., exoskeletons), teleoperated by human (e.g., tele-medical robot) and collaborate with human (e.g., mobile humanoid nursing robot), and (2) how human and robotic systems can synergistically work towards a shared goal to achieve high overall performance. Students will how to analyze and control the motion of human and robots, and to unite the knowledge in these two fields in the applications such as bio-inspired motion control, shared-autonomous control, and robot learning from human demonstrations. Students are expected to work individually on math problems, implement Matlab simulations, conduct literature review, and collaborate on course projects. Based on their background and research interest, students can choose among course projects that focus on mechanical design, control and learning algorithm, and studies on human motion and human-robot interaction.


Undergraduate Linear Algebra, kinematics and dynamics in robotics, basic statistics, and programming experience in Matlab.

Reference books:

Course Syllabus:

Course schedule:


  1. In-Class Participation and Preparation 10%
  2. Quizzes 20%
  3. Assignments 25%
  4. Literature review 10%
  5. Course project 35%
  6. In-Class Participation and Preparation 10%

Lecture Slides:

  1. Introduction to Course
  2. Introduction to Course Topics
  3. Introduction to Course Topics02
  4. Introduction to Course Project
  5. Introduction to Course Topics03
  6. Forward Kinematics
  7. Inverse Kinematics
  8. Kinematic Redundancy
  9. Stroke Rehabilitation: Gaming and Virtual Reality
  10. Upper Limb Exoskeleton for Stroke Rehabilitation
  11. EMG Sensing
  12. Feature Extraction
  13. Beyond EMG
  14. Neural Interface
  15. LfD Framework
  16. Social Learning
  17. Demonstration
  18. LowLevel Learning 01
  19. LowLevel Learning 02
  20. HighLevel Learning 01
  21. HighLevel Learning 02
  22. HighLevel Learning 03


The instructor reserves the right to modify the course outline and policies mentioned in this syllabus at any time during the term.