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Collaborative Task Planning and Learning through Language Communication in a Human-Robot Team

Collaborator: Interactive Robotics Group @ Massachusetts Institute of Technology

Motivations and Objectives


Most robotic planning systems operate based on a closed set of representations. However, in reality, robots are not likely to have a complete set of domain models and will often encounter new situations or new tasks they don’t have any knowledge or experience about.

Even given sufficient state and action representations, automated planning is still computationally challenging. Designing planners that can generate high-quality plans and perform efficiently across various domains remains an open problem.


The overall goal of this project is to eevelop a novel framework that tightly links language and dialogue processing with the robot’s underlying planning system to support collaborative task planning and learning in a human-robot team.


Style Learning

Help agents learn to behave according to others’ social conventions, enabling efficient teamwork.

Action-Effect Learning

Incorporating action effect learning in teaching robots new tasks through language communication.

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