16-832 Integrated Planning and Learning

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, each group potentially targeting its own reasons for doing so.

The course is structured to have several classes where the instructor teaches the material. The rest of the class, the students present papers from the list of papers compiled by the instructor. In addition, the students have to come up with and conduct a semester-long research project in the area of integrated planning and learning. They will be presented with a set of potential domains in the areas of manipulation, multi-agent coordination, and others, with the corresponding codebases and test benchmarks. The students are also free to choose their own domains based on their interests. The project is supposed to lead to a research paper worthy of publication at a top-tier robotics venue.

To take the class students should have good knowledge in classical and NN-based machine learning, learning-based control (Deep RL, Imitation Learning), planning and decision-making, and strong programming skills to complete the research project.

Spring 2026 Course Information

Announcements

Dates/times

Class meetings: Mondays, Wednesdays, 12:30-1:50PM, NSH 3002

Instructor

Who Email
Maxim Likhachev

Teaching Assistants

Who Email
Gopal Venkitachalam

Office Hours

Who Location Hours
Maxim NSH 3211 By appointment
Gopal NSH 1612 Tue 2:30-3:30PM and Thu 5-6PM

Grading

The criteria used to compute the final grade include the quality of the research project and participation in the class including the presentation of papers:

Research project 70%
In-class participation and paper presentations 30%


Class lectures/notes:

Tentative schedule posted here (PDF)

Date Topic Papers Slides Additional Info
1/12 (Mon) Introduction; Why Integrate Planning and Learning
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 slides
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1/14 (Wed) Integrating learning into planning: speeding up planning Part 1
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 slides
Test domains: MAPF, Manipulation
1/19 (Mon) MLK DAY: NO CLASS
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1/21 (Wed) Integrating learning into planning: speeding up planning Part 1 (cont'd)
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1/26 (Mon) Integrating learning into planning: learning cost function Part 1
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 slides
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1/28 (Wed) Integrating learning into planning: learning goal conditions Part 1
see schedule above
Open-World Task and Motion Planning, Survey of Optimization-based Task and Motion Planning_ From Classical To Learning Approaches
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2/2 (Mon) Integrating learning into planning: planning with imperfect world dynamics model Part 1
see schedule above
CMAX++, SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding
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2/4 (Wed) Integrating planning into learning: planning for learning long-horizon tasks Part 1
see schedule above
Deep Skill Graphs, SPIN: distilling Skill-RRT for long-horizon prehensile and non-prehensile manipulation
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2/9 (Mon) Integrating planning into learning: safe task achievement in learning-based control Part I
see schedule above
Primer on Diffusion, Motion Planning Diffusion
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2/11 (Wed) Integrating planning into learning: learning from planning Part I
see schedule above
Planning-Guided Diffusion Policy Learning for Bimanual Manipulation, Offline Imitation Learning Through Graph Search and Retrieval
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2/16 (Mon) Integrating planning into learning: improving inference process Part I
see schedule above
Stream of Search Learning to Search in Language
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2/18 (Wed) Project Proposal Presentations
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2/23 (Mon) Project Proposal Presentations (cont'd)
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2/25 (Wed) Learning to plan Part I
see schedule above
To be posted.
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