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Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation (2025)
Albert Yu
, Chengshu Li, Luca Macesanu, Arnav Balaji, Ruchira Ray,
Raymond Mooney
, Roberto Martín-Martín
Effective robotic systems for long-horizon human-robot collaboration must adapt to a wide range of human partners, whose physical behavior, willingness to assist, and understanding of the robot's capabilities may change over time. This demands a tightly coupled communication loop that grants both agents the flexibility to propose, accept, or decline requests as they coordinate toward completing the task effectively. We apply a Mixed-Initiative dialog paradigm to Collaborative human-roBot teaming and propose MICoBot, a system that handles the common scenario where both agents, using natural language, take initiative in formulating, accepting, or rejecting proposals on who can best complete different steps of a task. To handle diverse, task-directed dialog, and find successful collaborative strategies that minimize human effort, MICoBot makes decisions at three levels: (1) a meta-planner considers human dialog to formulate and code a high-level collaboration strategy, (2) a planner optimally allocates the remaining steps to either agent based on the robot's capabilities (measured by a simulation-pretrained affordance model) and the human's estimated availability to help, and (3) an action executor decides the low-level actions to perform or words to say to the human. Our extensive evaluations in simulation and real-world -- on a physical robot with 18 unique human participants over 27 hours -- demonstrate the ability of our method to effectively collaborate with diverse human users, yielding significantly improved task success and user experience than a pure LLM baseline and other agent allocation models. See additional videos and materials at https://robin-lab.cs.utexas.edu/MicoBot/.
View:
PDF
,
Arxiv
Citation:
Preprint
(2025).
Bibtex:
@article{yu:arxiv25, title={Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation}, author={Albert Yu and Chengshu Li and Luca Macesanu and Arnav Balaji and Ruchira Ray and Raymond Mooney and Roberto Martín-Martín}, journal={Preprint}, month={August}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=128128", year={2025} }
People
Raymond J. Mooney
Faculty
mooney [at] cs utexas edu
Albert Yu
Ph.D. Student
albertyu [at] utexas edu
Areas of Interest
Agent Modeling in Multiagent Systems
Connecting Language and Perception
Deep Learning
Human Robot Interaction
Language and Robotics
Language and Vision
Natural Language Processing
Robotics
Service Robots
Labs
Machine Learning