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Principles of Robot Autonomy II

Stanford University
California , United States
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Program Details
Degree
Masters
Major
Mechanical Engineering | Robotics Engineering | Artificial Intelligence
Area of study
Engineering
Course Language
English
About Program

Program Overview

Principles of Robot Autonomy II

Course Description

This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. Concepts that will be covered in the course are: Reinforcement Learning (RL) and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, as well as imitation learning and human intent inference. Students will learn the theoretical foundations for these concepts. Prerequisites: CS106A or equivalent, CME 100 or equivalent (for linear algebra), CME 106 or equivalent (for probability theory), and AA 174A/274A.


Instructors

  • Prof. Jeannette Bohg
  • Prof. Marco Pavone
  • Prof. Dorsa Sadigh

Course Assistants

  • Aditya Dutt
  • Suneel Belkhale
  • Chris Agia
  • Roger Dai

Meeting Times

Lectures meet on Mondays and Wednesdays from 1:30pm to 2:50pm at Skilling Auditorium.


Office Hours

  • Prof. Bohg's office hours are Wednesdays, 9:00am - 10:00am in Gates 244.
  • Prof. Pavone's office hours are Tuesdays 1:00pm - 2:00pm in Durand 261.
  • Prof. Sadigh's office hours are Fridays 9:00am - 10:00am in Gates 246 or by appointment.
  • CA office hours are:
    • Mondays from 2:30pm to 4:00pm (Suneel)
    • Mondays from 4:15pm to 5:30pm (Chris, Gates 100)
    • Tuesdays from 9:00am to 10:30am (Roger, Huang Basement)
    • Tuesdays from 4:30pm to 6:00pm (Aditya, Gates 100)
    • Thursdays from 2:30pm to 4:00pm (Suneel)
    • Fridays 2:30pm to 4:00pm (Aditya, Gates 100)

Syllabus

The class syllabus is available.


Project Report

For students taking the course for 4 units, a project report is required by the end of the quarter.


Schedule

Subject to change.


  • Week 1: Course overview, intro to ML for robotics, Neural networks and PyTorch tutorial
  • Week 2: Markov decision processes, Intro to RL
  • Week 3: Model-based and model-free RL for robot control
  • Week 4: Learning-based perception, Fundamentals of grasping and manipulation I
  • Week 5: Fundamentals of grasping and manipulation II, Learning-based grasping and manipulation
  • Week 6: Learning-based Manipulation, Interactive Perception
  • Week 7: Imitation learning I
  • Week 8: Imitation learning II, Learning from human feedback
  • Week 9: Interaction-aware learning, planning, and control, Shared autonomy
  • Week 10: Guest lecture (Erdem Bıyık), Guest lecture 2 (TBD)
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