By Peter Chipman, Digital Publication Specialist and OCW Educator Assistant
Robotics and artificial intelligence are fast-paced fields in which researchers constantly have to adapt to new technological developments. But in such fields, progress isn’t always achieved by competitive, individual effort; in many circumstances, cooperation and collaboration are more fruitful approaches. In the interview excerpt below, Brian Charles Williams, a professor at MIT’s Computer Science & Artificial Intelligence Laboratory, describes how he develops learning communities in the graduate-level course 16.412 Cognitive Robotics:
OCW: How is learning different in a course focused on an emerging field like cognitive robotics?
Brian Williams: Students are accustomed to reading chapters in textbooks—material that took decades for scientists to understand. But cognitive robotics is an active research area. It’s moving so quickly that every three years or so it reinvents itself. This course is focused on helping students close the gaps in the research. To be at the cutting edge of research, students need to read across papers and understand core ideas that are developed from a collection of publications. And then they need to be able to reduce that understanding to practice.
There’s also no better way to understand something than to teach it, implement it, and put it in a bigger context of some real-world application. That’s why we have a grand challenge at the center of the course experience.
OCW: Tell us more about the grand challenge.
Brian Williams: I like the idea of learning communities, of everybody trying to learn about a topic together. The grand challenge is a communal learning experience driven by a cutting-edge research question in cognitive robotics that allows us to focus on core reasoning algorithms. Students work in teams to present advanced lectures about different aspects of the topic.
OCW: Why teams?
Brian Williams: It’s important for students to work in teams because research is a collaborative endeavor. The notion that doctoral students are lone wolves is just not accurate. The more students can practice effective collaboration, the better.
It’s also the case that developing lectures is hard work. Just producing a first draft of a lecture can take 20 to 30 hours. And then you need to spend another 6 hours improving it. So, to develop a high-quality lecture, you really need two people working together.
OCW: How do you assess student work completed collaboratively?
Brian Williams: That is an interesting problem, because when the whole class does a project collaboratively teams can become too large. When that happens, people begin to feel disenfranchised. What I do to combat that is to make clear from the beginning what elements or materials individuals are responsible for contributing to the project. I have students write down what they are contributing so that I can assess their work accurately.
Another piece of the assessment puzzle is providing good feedback. The place where feedback matters the most is during the dry run for the students’ advanced lectures. A week before the students give their lecture to the class, they do a dry run for the teaching team and receive feedback. The process takes about two and a half hours. We teach them how to capture students’ interest at the beginning of the lecture and how to clarify the main points they want students to learn. We also help them convey the synergies between the main points and encourage them to consider the role of examples in their presentations.
OCW: Are there other components of the grand challenge, in addition to the advanced lectures?
Brian Williams: Yes. As I mentioned, the field of cognitive robotics is moving really fast. What normally happens is that members of the research community will generate tutorials on emerging themes. These tutorials encapsulate core ideas that everybody should know. The problem is that there’s just so much we need to know—but not enough time to write all the tutorials. So some of the students in the class are assigned to write tutorials related to the topic of the grand challenge. And a few others will write corresponding Jupiter or Python notebook problem sets. Along with the lectures, students end up producing materials that are enormously helpful to researchers in the field. This is important because I want them to learn that as scientists, their role is to consolidate ideas and to teach the community.
OCW: It’s interesting that you have the goal of figuring out cognitive robotics as a field, but also how to teach it to others.
Brian Williams: And how to catalyze community. An engaged, collaborative community is absolutely key.
You can read more of Professor Williams’s thoughts about teaching 16.412 on the Instructor Insights page of this course.
Keep learning! The following courses and Instructor Insights may be of interest to you:
Another OCW Course Offered by Professor Williams
This course surveys a variety of reasoning, optimization, and decision making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.
Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, and machine learning. Optimization paradigms include linear programming, integer programming, and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes.
More about Robotics and Artificial Intelligence
This course provides an overview of robot mechanisms, dynamics, and intelligent controls. Topics include planar and spatial kinematics, and motion planning; mechanism design for manipulators and mobile robots, multi-rigid-body dynamics, 3D graphic simulation; control design, actuators, and sensors; wireless networking, task modeling, human-machine interface, and embedded software. Weekly laboratories provide experience with servo drives, real-time control, and embedded software. Students design and fabricate working robotic systems in a group-based term project.
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
More on Learning Communities
In the rather idiosyncratic syllabus for this course, which goes into much more philosophical depth than such documents usually attain, Professor Stephan L. Chorover lays out the principles of the collaborative learning system that formed the basis of his approach to teaching.
Elizabeth Choe and Jaime Goldstein discuss the importance of cultivating a sense of community in the classroom, and explain how situating themselves as facilitators-of-learning, rather than omniscient givers-of-knowledge, communicated to students their respect for them as learners.
The Beatles lived an insulated life in the 1960s. They couldn’t go out without being mobbed. As a result, the four of them spent much of their time together, listening to and playing music. In that process, they were constantly learning from each other. Lecturer Teresa Neff discusses the centrality of group learning in her Instructor Insights for this course.
Find insights like these on many other teaching approaches at our Educator Portal.