7 embodied-AI and robotics papers accepted at RSS 2024, 6 with code.7 篇被 RSS 2024 接收的具身智能与机器人论文，其中 6 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。
ABS pairs an agile RL locomotion policy with a learned reach-avoid value network and a recovery policy that takes over near obstacles, so a quadruped can run fast without crashing.ABS 将敏捷的强化学习运动策略与学习的可达-规避价值网络及恢复策略结合，接近障碍时由恢复策略接管，让四足机器人高速奔跑而不碰撞。
@misc{he2024agile,
title = {{Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion}},
author = {Tairan He and Chong Zhang and Wenli Xiao and Guanqi He and Changliu Liu and Guanya Shi},
year = {2024},
eprint = {2401.17583},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.17583},
}
27 simulated tasks (12 locomotion, 15 whole-body manipulation) on a Unitree H1 with two dexterous hands; reports that DreamerV3, TD-MPC2, SAC and PPO fail most of them, especially long-horizon whole-body ones.27 个仿真任务（12 个运动、15 个全身操作），机器人为带两只灵巧手的 Unitree H1；报告 DreamerV3、TD-MPC2、SAC、PPO 在大多数任务上未达标，长时程全身协调任务尤甚。
@misc{sferrazza2024humanoidbench,
title = {{HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation}},
author = {Carmelo Sferrazza and Dun-Ming Huang and Xingyu Lin and Youngwoon Lee and Pieter Abbeel},
year = {2024},
eprint = {2403.10506},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.10506},
}
MDT is a diffusion transformer that learns versatile behaviour from multimodal goals with few language annotations, using two self-supervised objectives.MDT 是扩散 Transformer，借助两个自监督目标，在语言标注很少时从多模态目标学习多样行为。
A video VLM that reads the monocular RGB history plus the instruction and outputs the next navigation action directly, without maps, odometry or depth.基于视频的视觉语言模型：只看单目 RGB 历史帧和语言指令，直接输出下一步导航动作，不依赖地图、里程计或深度。
@misc{zhang2024navid,
title = {{NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation}},
author = {Jiazhao Zhang and Kunyu Wang and Rongtao Xu and Gengze Zhou and Yicong Hong and Xiaomeng Fang and Qi Wu and Zhizheng Zhang and He Wang},
year = {2024},
eprint = {2402.15852},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2402.15852},
}
Combines tactile perception with language so an embodied system can reason about physical properties learned by touch; contributes the PhysiCLeAR dataset.把触觉感知与语言结合，让具身系统对触摸获得的物理属性做常识推理；贡献 PhysiCLeAR 数据集。
@misc{yu2024octopi,
title = {{Octopi: Object Property Reasoning with Large Tactile-Language Models}},
author = {Samson Yu and Kelvin Lin and Anxing Xiao and Jiafei Duan and Harold Soh},
year = {2024},
eprint = {2405.02794},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2405.02794},
}
A recurrent graph network estimates object state and latent physics from visuo-tactile history; the learned dynamics model drives MPC for tasks like dense packing.循环图网络从视触觉历史估计物体状态与潜在物理属性，学到的动力学模型用于 MPC，完成密集装箱等任务。
@misc{ai2024robopack,
title = {{RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing}},
author = {Bo Ai and Stephen Tian and Haochen Shi and Yixuan Wang and Cheston Tan and Yunzhu Li and Jiajun Wu},
year = {2024},
eprint = {2407.01418},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.01418},
}
@misc{pumacay2024colosseum,
title = {{THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation}},
author = {Wilbert Pumacay and Ishika Singh and Jiafei Duan and Ranjay Krishna and Jesse Thomason and Dieter Fox},
year = {2024},
eprint = {2402.08191},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2402.08191},
}