4 embodied-AI and robotics papers accepted at NeurIPS 2024, 3 with code.4 篇被 NeurIPS 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。
Trains a large teacher only on synthetic images with precise depth, then pseudo-labels many real images to train smaller students; gives sharper, more robust monocular depth than V1.先只用深度精确的合成图像训练大教师模型，再给大量真实图像打伪标签来训练小模型；单目深度比 V1 更锐利、更鲁棒。
@misc{yang2024depth,
title = {{Depth Anything V2}},
author = {Lihe Yang and Bingyi Kang and Zilong Huang and Zhen Zhao and Xiaogang Xu and Jiashi Feng and Hengshuang Zhao},
year = {2024},
eprint = {2406.09414},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2406.09414},
}
@misc{zhu2024point,
title = {{Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning}},
author = {Haoyi Zhu and Yating Wang and Di Huang and Weicai Ye and Wanli Ouyang and Tong He},
year = {2024},
eprint = {2402.02500},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2402.02500},
}
An end-to-end VLA built on Mamba state space models, aiming for both robotic reasoning and cheap fine-tuning and inference.基于 Mamba 状态空间模型的端到端 VLA，兼顾机器人推理能力和较低的微调与推理成本。
@misc{liu2024robomamba,
title = {{RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation}},
author = {Jiaming Liu and Mengzhen Liu and Zhenyu Wang and Pengju An and Xiaoqi Li and Kaichen Zhou and Senqiao Yang and Renrui Zhang and Yandong Guo and Shanghang Zhang},
year = {2024},
eprint = {2406.04339},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2406.04339},
}
HPT: per-robot stems turn proprioception and vision into a few tokens for one shared transformer trunk, pretrained on 52 datasets (robot, sim, human video); reports 20%+ gains on unseen tasks.HPT：为每种机器人设计“输入头”，把本体感知与视觉映射成短 token 序列，送入共享的 Transformer 主干；在 52 个数据集（真机、仿真、人类视频）上预训练，报告在未见任务上微调提升 20% 以上。