@COMMENT This file was generated by bib2html.pl <http://www.cs.cmu.edu/~pfr/misc_software/index.html#bib2html> version 0.90
@COMMENT written by Patrick Riley <http://www.cs.cmu.edu/~pfr>
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings{yoonwoo_kim_ICRA2026,
  author   = {Yoonwoo Kim and Raghav Arora and Roberto Martín-Martín and Peter Stone and Ben Abbatematteo and Yoonchang Sung},
  title    = {Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning},
  booktitle = {International Conference on Robotics and Automation},
  year     = {2026},
  month    = {June},
  location = {Vienna, Austria},
  abstract = {Robot planning in partially observable environments, where not all objects are known or visible, is a challenging problem, as it requires reasoning under uncertainty through partially observable Markov decision processes. During the execution of a computed plan, a robot may unexpectedly observe task-irrelevant objects, which are typically ignored by naive planners. In this work, we propose incorporating two types of common-sense knowledge: (1) certain objects are more likely to be found in specific locations; and (2) similar objects are likely to be co-located, while dissimilar objects are less likely to be found together. Manually engineering such knowledge is complex, so we explore leveraging the powerful common-sense reasoning capabilities of large language models (LLMs). Our planning and execution framework, CoCo-TAMP, introduces a hierarchical state estimation that uses LLM-guided information to shape the belief over task-relevant objects, enabling efficient solutions to long-horizon task and motion planning problems. In experiments, CoCo-TAMP achieves an average reduction of 62.7 percent in planning and execution time in simulation, and 72.6 percent in real-world demonstrations, compared to a baseline that does not incorporate either type of common-sense knowledge.},
}