101 citations · 230 across the 19 of their papers we have counts for
19 papers
SkillWrapper: Generative Predicate Invention for Task-level Robot Planning
Ziyi Yang, Benned Hedegaard, Ahmed Jaafar +8
Generalizing from individual skill executions to long-horizon tasks is a core challenge in building autonomous robots. A promising direction is learning high-level, symbolic repres…
Skill Generalization with Verbs
Rachel Ma, Lyndon Lam, Benjamin A. Spiegel +6
It is imperative that robots can understand natural language commands issued by humans. Such commands typically contain verbs that signify what action should be performed on a give…
Bootstrapping Object-level Planning with Large Language Models
David Paulius, Alejandro Agostini, Benedict Quartey +1
We introduce a new method that extracts knowledge from a large language model (LLM) to produce object-level plans, which describe high-level changes to object state, and uses them…
CAPE: Corrective Actions from Precondition Errors using Large Language Models
Shreyas Sundara Raman, Vanya Cohen, Ifrah Idrees +4
Extracting commonsense knowledge from a large language model (LLM) offers a path to designing intelligent robots. Existing approaches that leverage LLMs for planning are unable to…
Approximate Task Tree Retrieval in a Knowledge Network for Robotic Cooking
Md. Sadman Sakib, David Paulius, Yu Sun
Flexible task planning continues to pose a difficult challenge for robots, where a robot is unable to creatively adapt their task plans to new or unseen problems, which is mainly d…
Long-Horizon Planning and Execution with Functional Object-Oriented Networks
David Paulius, Alejandro Agostini, Dongheui Lee
Following work on joint object-action representations, functional object-oriented networks (FOON) were introduced as a knowledge graph representation for robots. A FOON contains sy…