3 embodied-AI and robotics papers accepted at ICLR 2024, 3 with code.3 篇被 ICLR 2024 接收的具身智能与机器人论文,其中 3 篇附代码。
arXiv papers are listed when their arXiv comment says they were accepted (e.g. 'Accepted to CoRL 2026'); papers that do not say so are missing, and the list grows as authors post camera-ready versions. Curated entries carry a hand-checked venue. For CoRL 2026 the official program is used as well.arXiv 论文仅在其 arXiv 备注写明已被接收(如 “Accepted to CoRL 2026”)时列入;未注明的论文不会出现,列表会随作者上传终稿而增加。精选条目的会议/期刊信息经过人工核对。CoRL 2026 另依据官方日程。
Adds simulated humanoid avatars and a human-in-the-loop interface to Habitat, so robots can be trained and evaluated on tasks shared with people, such as social navigation and social rearrangement.为 Habitat 加入仿真人形化身和真人在环交互接口,可在与人协作的任务(如社交导航、社交重排)上训练和评测机器人。
@misc{puig2023habitat,
title = {{Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots}},
author = {Xavier Puig and Eric Undersander and Andrew Szot and Mikael Dallaire Cote and Tsung-Yen Yang and Ruslan Partsey and Ruta Desai and Alexander William Clegg and Michal Hlavac and So Yeon Min and Vladimír Vondruš and Theophile Gervet and Vincent-Pierre Berges and John M. Turner and Oleksandr Maksymets and Zsolt Kira and Mrinal Kalakrishnan and Jitendra Malik and Devendra Singh Chaplot and Unnat Jain and Dhruv Batra and Akshara Rai and Roozbeh Mottaghi},
year = {2023},
eprint = {2310.13724},
archivePrefix = {arXiv},
primaryClass = {cs.HC},
url = {https://arxiv.org/abs/2310.13724},
}
Differentiable tactile simulator with an FEM soft-body elastomer model, built for contact with many material types rather than mostly rigid objects.可微触觉仿真器,用 FEM 软体模型模拟弹性体,面向多种材料的接触,而非主要针对刚体。
@misc{si2024difftactile,
title = {{DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation}},
author = {Zilin Si and Gu Zhang and Qingwei Ben and Branden Romero and Zhou Xian and Chao Liu and Chuang Gan},
year = {2024},
eprint = {2403.08716},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.08716},
}
GR-1 — a GPT-style model that predicts both actions and future frames; pre-training on Ego4D video prediction before robot fine-tuning lifts CALVIN unseen-scene success from 53.3% to 85.4% (as reported).GR-1:GPT 式模型同时预测动作与未来画面;先在 Ego4D 上做视频预测预训练、再用机器人数据微调,论文报告 CALVIN 未见场景成功率从 53.3% 提升到 85.4%。
@misc{wu2023unleashing,
title = {{Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation}},
author = {Hongtao Wu and Ya Jing and Chilam Cheang and Guangzeng Chen and Jiafeng Xu and Xinghang Li and Minghuan Liu and Hang Li and Tao Kong},
year = {2023},
eprint = {2312.13139},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.13139},
}