机器人 / 具身智能

SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels

Malte Mosbach, Jan Niklas Ewertz, Angel Villar-Corrales, Sven Behnke

专题命中 机器人操作 ：manipulation(title,abstract)；world model(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.LG

Modality-Driven Design for Multi-Step Dexterous Manipulation: Insights from Neuroscience

Naoki Wake, Atsushi Kanehira, Daichi Saito, Jun Takamatsu, Kazuhiro Sasabuchi, Hideki Koike, Katsushi Ikeuchi

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.CV

Comments 8 pages, 5 figures, 2 tables. Last updated on December 14th, 2024

HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation

Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin, Youngwoon Lee, Pieter Abbeel

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract)；robot learning(abstract)；分类 cs.RO、cs.AI、cs.LG

SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes

Ninad Khargonkar, Sai Haneesh Allu, Yangxiao Lu, Jishnu Jaykumar P, Balakrishnan Prabhakaran, Yu Xiang

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments Accepted to ICRA 2024. Project page is available at https://irvlutd.github.io/SceneReplica

HomeRobot: Open-Vocabulary Mobile Manipulation

Sriram Yenamandra, Arun Ramachandran, Karmesh Yadav, Austin Wang, Mukul Khanna, Theophile Gervet, Tsung-Yen Yang, Vidhi Jain, Alexander William Clegg, John Turner, Zsolt Kira, Manolis Savva, Angel Chang, Devendra Singh Chaplot, Dhruv Batra, Roozbeh Mottaghi, Yonatan Bisk, Chris Paxton

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract)；navigation(abstract)；分类 cs.RO、cs.AI、cs.CV

Comments 37 pages, 22 figures, 8 tables

RoboSense At Edge: Detecting Slip, Crumple and Shape of the Object in Robotic Hand for Teleoprations

Sudev Kumar Padhi, Mohit Kumar, Debanka Giri, Subidh Ali

专题命中 机器人操作 ：robotic(title,abstract)；robotics(abstract)；manipulation(abstract)；分类 cs.RO、cs.AI、cs.LG

Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation

Fabian Schmalstieg, Daniel Honerkamp, Tim Welschehold, Abhinav Valada

专题命中 机器人操作 ：manipulation(title,abstract)；embodied AI(abstract)；navigation(abstract)；分类 cs.RO、cs.AI、cs.LG

Comments 11 pages, 6 figures, Accepted for publication in RA-L. Code and Models: http://himos.cs.uni-freiburg.de/

Language to Rewards for Robotic Skill Synthesis

Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia

专题命中 机器人操作 ：robotic(title,abstract)；robotics(abstract)；manipulation(abstract)；分类 cs.RO、cs.AI、cs.LG

Comments https://language-to-reward.github.io/

FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation

Zhou Xian, Bo Zhu, Zhenjia Xu, Hsiao-Yu Tung, Antonio Torralba, Katerina Fragkiadaki, Chuang Gan

专题命中 机器人操作 ：manipulation(title,abstract)；robot learning(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.LG

Towards Confidence-guided Shape Completion for Robotic Applications

Andrea Rosasco, Stefano Berti, Fabrizio Bottarel, Michele Colledanchise, Lorenzo Natale

专题命中 机器人操作 ：robotic(title,abstract)；manipulation(abstract)；navigation(abstract)；分类 cs.RO、cs.CV、cs.LG

Journal ref IEEE International Conference on Humanoid Robots, 2022

Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation

Albert Zhan, Ruihan Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin

专题命中 机器人操作 ：robotic(title,abstract)；robot learning(abstract)；manipulation(abstract)；分类 cs.RO、cs.AI、cs.LG

VisuoSpatial Foresight for Physical Sequential Fabric Manipulation

Ryan Hoque, Daniel Seita, Ashwin Balakrishna, Aditya Ganapathi, Ajay Kumar Tanwani, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.CV

Comments Journal extension of prior work on VSF to appear in Autonomous Robots S.I. 207. arXiv admin note: text overlap with arXiv:2003.09044

REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning

Brian Yang, Jesse Zhang, Vitchyr Pong, Sergey Levine, Dinesh Jayaraman

专题命中 机器人操作 ：robotic(title,abstract)；robotics(abstract)；manipulation(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments Extended version of paper accepted to ICRA 2019

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, Wojciech Zaremba

专题命中 机器人操作 ：robotics(title,abstract)；manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.LG

Representation Handoffs for OpenArm-Based Laboratory Mobile Manipulation

基于OpenArm的实验室移动操作的表示交接

Yang Shen, Chonghao Cheng, Ziyi Zhao, Jialuo Zhu, Zhenyi Yi, Qi Zhao, Jian Yang, Yuhui Shi, Chin-Teng Lin

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract,comments)；embodied AI(abstract)；分类 cs.RO、cs.AI

AI总结 本研究提出基于OpenArm的实验室移动操作原型，通过表示交接整合多模块，可暴露部署阻碍并为具身系统整合提供调试接口。

Comments Robotics: Science and Systems (RSS) Workshop 2026

ABot-M0.5: Unified Mobility-and-Manipulation World Action Model

ABot-M0.5：统一移动与操作的世界动作模型

Ronghan Chen, Yandan Yang, Zuojin Tang, Dongjie Huo, Tong Lin, Haoning Wu, Haoyun Liu, Yuzhi Chen, Lulu Zheng, Botai Yuan, Tianlun Li, Mingxin Wang, Dekang Qi, Bin Hu, Wei Mei, Yuze Xuan, Haolong Yang, Yanqing Zhu, Mu Xu, Zhiheng Ma, Xinyuan Chang

机构 * AMAP CV Lab（AMAP CV实验室） 纠错

专题命中 机器人操作 ：manipulation(title,abstract)；navigation(abstract)；world model(abstract)；分类 cs.RO、cs.CV

AI总结 针对移动操作任务中VLA策略缺乏世界建模、动作空间耦合及训练-推理不一致的问题，提出ABot-M0.5模型，通过三级对齐（时间粒度、动作空间、训练-测试一致性）实现细粒度控制，在长时域任务成功率和控制精度上达到最优。

Comments Code: https://github.com/amap-cvlab/ABot-Manipulation

FAR-Dex: Few-shot Data Augmentation and Adaptive Residual Policy Refinement for Dexterous Manipulation

FAR-Dex: 少样本数据增强与自适应残差策略细化用于灵巧操作

Yushan Bai, Fulin Chen, Hongzheng Sun, Yuchuang Tong, En Li, Zhengtao Zhang

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract,comments)；robotic(abstract)；分类 cs.RO、cs.AI

AI总结 FAR-Dex通过少样本数据增强和自适应残差策略细化，提升机械臂与手的协同精度，实现高成功率的灵巧操作任务。

Comments Accepted to IEEE International Conference on Robotics and Automation (ICRA) 2026

ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation

Enyu Zhao, Vedant Raval, Hejia Zhang, Jiageng Mao, Zeyu Shangguan, Stefanos Nikolaidis, Yue Wang, Daniel Seita

机构 * Department of Computer Science, University of Southern California（计算机科学系，南加州大学） 纠错

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract)；robotic(abstract)；分类 cs.RO、cs.AI

Comments Conference on Robot Learning (CoRL) 2025. 50 pages and 30 figures. v2 is the camera-ready and includes a few more new experiments compared to v1

Make a Donut: Hierarchical EMD-Space Planning for Zero-Shot Deformable Manipulation with Tools

Yang You, Bokui Shen, Congyue Deng, Haoran Geng, Songlin Wei, He Wang, Leonidas Guibas

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract,comments)；robotic(abstract)；分类 cs.RO、cs.AI

Comments 8 pages. IEEE Robotics and Automation Letters (RA-L). Preprint Version. Accepted January, 2025

LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments

Federico Ceola, Lorenzo Natale, Niko Sünderhauf, Krishan Rana

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract,comments)；robotic(abstract)；分类 cs.RO、cs.AI

Comments RSS 2024 Workshop on Data Generation for Robotics

Recasting Generic Pretrained Vision Transformers As Object-Centric Scene Encoders For Manipulation Policies

Jianing Qian, Anastasios Panagopoulos, Dinesh Jayaraman

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract,comments)；robotic(abstract)；分类 cs.RO、cs.CV

Comments Accepted to International Conference on Robotics and Automation(ICRA) 2024

A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing

Kangcheng Liu

专题命中 机器人操作 ：robotics(title,comments)；manipulation(abstract)；navigation(abstract)；robotic(abstract)

Comments International Conference on Robotics and Automation (ICRA) 2022, 6 Figures. arXiv admin note: substantial text overlap with arXiv:2312.01262

A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction

Gamze Akyol, Sanem Sariel, Eren Erdal Aksoy

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract,comments)；robot learning(abstract)；分类 cs.RO、cs.CV

Comments Accepted for publication in the Proceedings of 2021 20th International Conference on Advanced Robotics (ICAR)

DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation

Mike Lambeta, Po-Wei Chou, Stephen Tian, Brian Yang, Benjamin Maloon, Victoria Rose Most, Dave Stroud, Raymond Santos, Ahmad Byagowi, Gregg Kammerer, Dinesh Jayaraman, Roberto Calandra

专题命中 机器人操作 ：manipulation(title,abstract)；robotics(abstract,comments)；robotic(abstract)；分类 cs.RO、cs.LG

Comments 8 pages, published in the IEEE Robotics and Automation Letters (RA-L)

PDDL-ART: Autonomous Symbolic Abstraction From Demonstration For Long-Horizon Robotic Manipulation Using Vision-Language Models

PDDL-ART：基于演示的自主符号抽象方法，用于使用视觉语言模型的长时程机器人操纵

Disha Kamale, Dmitry Berenson

机构 * University of Michigan（密歇根大学） 纠错

专题命中 机器人操作 ：manipulation(title,abstract)；robotic(title)；分类 cs.RO

AI总结 PDDL-ART是一种基于VLM的框架，可自主从演示生成PDDL描述，经多阶段校正确保语义对齐，在发动机维护和家庭操纵任务上平均成功率达93.3%，优于基线规划器。

Sparse Meets Dense: Correspondence Guided Robotic Manipulation with Rigid-Deformable Interactions

稀疏与稠密结合：基于对应关系的刚-柔交互机器人操纵方法

Ziyu Zhu, Yue Chen, Xirui Liang, Hojin Bae, Yuran Wang, Zhen Yuan, Ruihai Wu, Hao Dong

机构 * School of Computer Science, PKU（北京大学计算机学院） ； School of EECS, PKU（北京大学电子工程与计算机科学学院） ； CFCS（计算与数字科学中心） 纠错

专题命中 机器人操作 ：manipulation(title,abstract)；robotic(title)；分类 cs.RO

AI总结 针对刚-柔交互操纵任务，提出混合对应关系表征方法，结合稀疏关键点与稠密对应关系，实现少量演示下的新任务一次性迁移，实验验证其有效性与适用性。

Comments ICRA 2026 conference paper

PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation

PrimitiveVLA：学习可复用的运动基元以实现高效且可泛化的机器人操作

Yutai Li, Shaohui Peng, Jiaming Guo, Di Huang, Zihao Zhang, Yuxuan Guo, Yunkai Gao, Siming Lan, Ling Li, Xing Hu, Yunji Chen

机构 * State Key Lab of Processors, Institute of Computing Technology, CAS（处理器国家重点实验室，计算技术研究所，中国科学院） ； Jiangsu Key Laboratory of AI for Industries, Institute of AI for Industries, CAS（江苏人工智能工业重点实验室，人工智能工业研究所，中国科学院） ； University of Chinese Academy of Sciences（中国科学院大学） ； Cambricon Technologies（寒武科技） ； Intelligent Software Research Center, Institute of Software, CAS（软件研究所智能软件研究中心，中国科学院） ； University of Science and Technology of China（中国科学技术大学） 纠错

专题命中 机器人操作 ：robotic(title,abstract)；manipulation(title)；分类 cs.RO

AI总结 提出PrimitiveVLA框架，通过将视觉-语言-动作模型从直接指令到控制映射转向以基元为中心的拆解与组装范式，利用多模态规范表示和自动化流水线，提升数据效率并实现零样本泛化。

Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation

用于接触丰富的机器人操作的表示对齐触觉基础

Ruilin Chen, Jingkai Jia, Tong Yang, Xinyu Zhou, Qiao Sun, Jiangwei Zhong, Shizeng Zhang, Nuo Chen, Bailin He, Wei Li, Wenqiang Zhang

机构 * Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院智能信息处理上海市重点实验室） ； College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学智能机器人与先进制造学院） ； Lenovo CTO Organization（联想首席技术官组织） ； Nanyang Technological University（南洋理工大学） ； TeleAl, China Telecom（中国电信天翼人工智能公司） 纠错

专题命中 机器人操作 ：manipulation(title,abstract)；robotic(title)；分类 cs.RO

AI总结 研究接触丰富的机器人操作中，针对VLA策略不同表示作用下触觉监督应用位置不明的问题，通过线性探针分析找到可预测未来触觉状态的中间动作专家特征，引入LTP，实验证明表示对齐的触觉基础效果更佳。

RoboNav-Arm: Agentic AI-Driven Navigation and Obstacle Avoidance for Robotic Manipulator in Cluttered Environments

RoboNav-Arm：杂乱环境中机器人操纵器的智能AI驱动导航与避障

Aachal Sharma, Narendra Kumar Dhar

机构 * Centre for Artificial Intelligence and Robotics (CAIR), Indian Institute of Technology Mandi（人工智能与机器人中心（CAIR），印度曼迪理工学院） 纠错

专题命中 机器人操作 ：robotic(title,abstract)；navigation(title)；分类 cs.RO

AI总结 针对杂乱环境中机器人操纵器面临的挑战，提出含环境、中央协调和规划模块的安全任务执行框架，能实时检测与定位障碍物，依环境选算法规划轨迹并优化，在Gazebo Classic中评估，展现动态场景鲁棒性。

Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation

偏好校准的人机协同强化学习用于机器人操作

Zeyi Liu, Guangyao Liu, Yinuo Qu, Yuquan Xue, Bofang Jia, Chunhua Yang, Weihua Gui, Keke Huang, Ziwei Wang

机构 * Central South University（中南大学） ； Nanyang Technological University（南洋理工大学） ； Zhejiang University（浙江大学） 纠错

专题命中 机器人操作 ：manipulation(title,abstract)；robotic(title)；分类 cs.RO

AI总结 提出PACT框架，通过干预隐式偏好信号进行信用重分配和策略对齐，提升人机协同强化学习的样本效率和性能。