16-832 Special Topic: Integrated Planning and Learning
12 units, Carnegie Mellon University
Planning has always been one of the central components in autonomy stacks of robotic systems, ranging from self-driving vehicles and autonomous drones to mobile manipulation platforms and quadrupeds to multi-agent robotic systems. Learning-based control such as Deep RL and Imitation Learning has also emerged as one of the powerful, and potentially alternative, approaches to controlling various robotic systems, especially the ones that need to interact with their environment. These two methodologies to robot control have their own strengths and weaknesses but are often complementary. Planning provides strong guarantees on performance and safety but can be computationally intensive and depends heavily on the quality of a model, which is often hard to attain when interaction with environment is involved. Learning-based control is often faster and removes dependency on a model but depends heavily on the adequacy of the training data. This complementarity of the approaches leads to strong interest in combining them, both within academia and industry. This class studies the latest algorithmic approaches to integrated planning and learning. In particular, the class first examines different reasons for combining the two methodologies, from computational reasons to model dependency to the duration of the development cycle of a fielded robotic system. It then studies state-of-the-art research within each group of algorithmic approaches to integrating planning and learning. The course will be structured to have several classes where the instructor teaches the material. The rest of the class, students will be asked to present papers from the list compiled by the instructor. In addition, students will conduct a semester-long research project in the area of integrated planning and learning. The project is supposed to lead to a research paper worthy of publication at a top-tier robotics venue.
Prerequisites: none listed
Offered: Spring 2027, Spring 2026
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