license: mit
task_categories:
- robotics
language:
- en
tags:
- IROS
- Embodied
IROS 2023 Accepted Paper Meta Info Dataset
This dataset is collect from the IROS 2023-2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (https://ieeexplore.ieee.org/xpl/conhome/10341341/proceeding) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/iros2023). For researchers who are interested in doing analysis of IROS 2023 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the IROS 2023 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Prototypical Contrastive Transfer Learning for Multimodal Language Understanding",
"detail_url": "https://ieeexplore.ieee.org/document/10341388/",
"author_list": ["Seitaro Otsuki", "Shintaro Ishikawa", "Komei Sugiura", "Seitaro Otsuki", "Shintaro Ishikawa", "Komei Sugiura"],
"abstract": "Although domestic service robots are expected to assist individuals who require support, they cannot currently interact smoothly with people through natural language. For example, given the instruction \u201cBring me a bottle from the kitchen,\u201d it is difficult for such robots to specify the bottle in an indoor environment. Most conventional models have been trained on real-world datasets that are labor..."
}
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