Talk Through It: End User Directed Manipulation Learning-Reference-Cited by-同舟云学术

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Talk Through It: End User Directed Manipulation Learning

Published:2024-09 Issue:9 Volume:9 Page:8051-8058
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Winge Carl¹^ORCID,Imdieke Adam¹^ORCID,Aldeeb Bahaa¹,Kang Dongyeop¹,Desingh Karthik¹^ORCID

Affiliation:

1. Minnesota Robotics Institute, University of Minnesota, Minneapolis, MN, USA

Funder

MnRI Seed Grant from the Minnesota Robotics Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7083369/10601335/10608414.pdf?arnumber=10608414

Reference30 articles.

1. RT-2: Vision-language-action models transfer web knowledge to robotic control;Brohan,2023

2. BC-Z: Zero-shot task generalization with robotic imitation learning;Jang,2021

3. Perceiver-actor: A multi-task transformer for robotic manipulation;Shridhar,2022

4. Act3D: 3D feature field transformers for multi-task robotic manipulation;Gervet,2023

5. Open-world object manipulation using pre-trained vision-language model;Stone,2023

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