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Learning to Ground Objects for Robot Task and Motion Planning

v1v2 (latest)

Learning to Ground Objects for Robot Task and Motion Planning

IEEE Robotics and Automation Letters (RA-L), 2022

14 February 2022

ArXiv (abs)PDF HTML

Papers citing "Learning to Ground Objects for Robot Task and Motion Planning"

5 / 5 papers shown

Effort Allocation for Deadline-Aware Task and Motion Planning: A
Metareasoning Approach

Effort Allocation for Deadline-Aware Task and Motion Planning: A Metareasoning Approach

Shahaf S. Shperberg

Peter Stone

296

0

0

08 Oct 2024

DKPROMPT: Domain Knowledge Prompting Vision-Language Models for
Open-World Planning

DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning

Zainab Altaweel

304

14

0

25 Jun 2024

Symbolic State Space Optimization for Long Horizon Mobile Manipulation
Planning

Symbolic State Space Optimization for Long Horizon Mobile Manipulation PlanningIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2023

222

6

0

21 Jul 2023

Grounding Classical Task Planners via Vision-Language Models

Grounding Classical Task Planners via Vision-Language Models

200

26

0

17 Apr 2023

Visually Grounded Task and Motion Planning for Mobile Manipulation

Visually Grounded Task and Motion Planning for Mobile ManipulationIEEE International Conference on Robotics and Automation (ICRA), 2022

194

22

0

22 Feb 2022