CIS 700: Integrated Intelligence for Robotics

Fall 2015, University of Pennsylvania

Instructor:  Eric Eaton, Ph.D.

Schedule

The following schedule lists the tentative dates, presenters, and readings for each topic in this course.  Topics and readings are subject to change.  We may end up shifting topics slightly earlier or later in the schedule, depending on unanticipated events.  This is especially the case for topics colored in blue, which have very tentative dates at this point and are more likely to shift than topics coming up soon.

Course Description

In order for robots to operate alongside humans in complex, unstructured, uncertain environments, they require substantial intelligence. However, the field of artificial intelligence (AI) has fragmented into various subfields, each studying different aspects of intelligence in relative isolation. The problem of designing intelligent robotic systems that persist in everyday environments provides an opportunity to reintegrate these different aspects of AI into a complete intelligent system.

In this project-based seminar course, students will study and develop an intelligent personal robot assistant, integrating perception, manipulation, learning, planning, and interaction. The resulting versatile robot will be capable of learning and performing a variety of tasks in real-world environments and collaborating effectively with humans. In addition, students will study a variety of advanced AI topics, including high-level perception and reasoning, scalable knowledge representation, lifelong/multi-task learning, integration of perception and control, learning from demonstration, and human-robot interaction.

Prerequisites

• CIS 519 or 520 - (Intro. to) Machine Learning
• CIS 521 - Fundamentals of AI
• CIS 580 or 581 - Machine Perception or Computer Vision
• ESE 650 - Learning in Robotics
• MEAM 520 - Introduction to Robotics

Course Format

This course will include two major components:

1. A seminar-style discussion of various topics in integrated intelligence
• We will study a variety of advanced AI techniques for perception, manipulation, reasoning and learning, and methods for integrating these techniques together into a complete intelligent system. 
  
• Students will be expected to come to each class prepared to discuss the required reading, having prepared reading summaries.
• Students will take turns leading the discussion.  Discussion leaders will also be responsible for preparing and delivering a brief presentation on the daily topic.


2. A semester-long project developing an integrated intelligent personal assistant robot
• Students will work in interdisciplinary teams (balancing backgrounds in AI, control, mechatronics, etc.) to design and build intelligent personal assistant robots that are capable of performing a variety of tasks in real-world environments and interacting with humans. 
  
• These robots will be expected to operate in the uncontrolled environments of the hallways, elevators, and various rooms of the engineering buildings at Penn.
  
• There will be various milestones, including an initial proposal, design, status reports/demos, and a final report with demonstration.
• In addition to developing the intelligent robot, this project will give students experience with developing research proposals, crafting development plans, conducting literature reviews, conducting evaluations, and presenting research results.
• Further details on the project will be discussed during the first class.
  

Syllabus

We will study the following topics:

• The Robot Operating System (ROS)
• Case studies in integrated AI:  Stanford STAIR, DARPA urban & robotics challenges, RoboCup, Robo Brain Project, UPenn MAGIC, CMU CoBots
• Scalable learning:  Online learning, transfer and multi-task learning
• Integrating perception, learning & control: architectures, deep learning for object recognition/scene understanding, and deep reinforcement learning
• Novel object recognition & manipulation: novel object grasping and manipulation, grasping in cluttered environments
• Localization & hierarchical planning: localization and HTN planning in continuous deployment, anytime path planning/replanning, planning in dynamic environments
• Human-Robot Interaction: collaborative problem solving, learning from demonstration
• Evaluation methodologies and experiment design
• Social, economic, safety, & privacy considerations/ethics of personal robots
• Project formulation, motivation, design, and presentation

These topics and due dates are all subject to change. Readings for each of these topics will include a variety of journal articles, conference papers, and technical reports.

Key Due Dates

• Wed 9/16 - Project Proposals / Getting Started
• Wed 10/7 - Project Design
  
• Mon 10/26 - Intermediate Milestones and Documentation
• TBA - Final Project Showcase & Report Due
  

In addition to the major project milestone dates:

• Reading summaries on the current day's topic will be collected at the start of class on a random schedule
  
• Reading journals will be examined on a random schedule, so keep them up-to-date!

Instructor

INSTRUCTOR

Eric Eaton, Ph.D.

Course Policies

Communication

Attendance and active participation are expected in every class. Participation includes asking questions, contributing answers, proposing ideas, and providing constructive comments.

As you will discover, I am a proponent of two-way communication and I welcome feedback during the semester about the course. I am available to answer student questions, listen to concerns, and talk about any course-related topic (or otherwise!). Come to office hours! This helps me get to know you. You are welcome to stop by and chat. There are many more exciting topics to talk about that we won't have time to cover in-class.

Whenever you e-mail me, be sure to use a meaningful subject line and include the phrase "[CIS 700]" at the beginning of the subject line. Your e-mail will catch my attention and I will respond quicker if you do this. I make an effort to respond to e-mails within 24 hours on weekdays and 48 hours on weekends.  However, unless it is a private matter, you should be posting your questions/issues to Piazza.

Although computer science and robotics work can be intense and solitary, please stay in touch with me and the other students in the course, particularly if you feel stuck on a topic or project and can't figure out how to proceed. Often a quick e-mail, face-to-face conference, or Piazza post can reveal solutions to problems and generate renewed creative and scholarly energy. It is essential that you begin assignments and projects early, since we will be covering a variety of challenging topics in this course.

• All questions about course material, assignments, and the project should be posted to Piazza, rather than e-mailed to the instructor.  Other students invariably have the same questions as you do, and posting them to Piazza encourages discussion.
• A major part of this course is collaboration both within and among teams.  Piazza can be a great way to problem-solve as a group.
  
• Post questions publicly as much as possible (you can set your posts to be anonymous to your classmates).
• Answer your classmates' posts.  Answering questions on Piazza is a great way to learn, and it counts for participation credit. 
• Don't forget to search the message board before posting!  Your question may have been already asked and answered.
  

Grading

Your grade will be based upon your paper summaries and reading journal, topic presentations, seminar participation, and the semester project.  All assignments must be submitted according to the assignment submission instructions.

At the end of the semester, final grades will be calculated as a weighted average of all grades according to the following weights:

All graded work will receive a percentage grade between 0% and 100%.  Here is how the percentage grades will map to final letter grades; percentages are not rounded:

Academic Integrity

Submission and Late Policy

Collaboration Policy

I want to encourage you to discuss the material and work together to understand it. Here are my thoughts on collaborating with other students, faculty, etc.:

• The readings and seminar topics are GROUP WORK. Please discuss the readings and associated topics with each other. Work together to understand the material.

• The reading summaries and journal are INDIVIDUAL WORK.  You are not permitted to work together on preparing (i.e., writing up) the summaries (although you may discuss the readings to understand them) -- these must be your own individual work.

• The Getting Started Task is ISOLATED TEAM WORK. Teams are NOT permitted to share code for the Getting Started Task, but may ask high-level questions of other teams.
  

• Although each team will be judged on its own performance, you are encouraged to collaborate across teams on the project.  Share the difficulties you're having with each other, and work together to solve problems.  However, you must credit the help from others!

• You are also permitted to collaborate with people outside of this course on the project.  Just remember that the project is ultimately your responsibility, and you will be graded based on YOUR effort, not the effort of anyone external to your team.
  

• You are permitted to use and consult code and other materials found on the internet for any aspect of this course, but you must provide proper attribution.
  

• Any written aspect of the project is ISOLATED TEAM WORK -- the written documents must be completed solely by members of your team.

Summary table:

If you have any questions as to what types of collaborations are allowed and which are dishonest, please ask me before you make a mistake.

Electronic Devices