Robotic Manipulation
Robotic manipulation research focuses on enabling robots to dexterously interact with their environment, achieving complex tasks through precise movements and object manipulation. Current efforts concentrate on improving the robustness and generalization of manipulation policies, often leveraging vision-language models, transformer architectures, and reinforcement learning techniques to enable robots to understand and respond to diverse instructions and environments. This field is crucial for advancing automation in various sectors, from manufacturing and logistics to healthcare and agriculture, by creating more adaptable and reliable robotic systems capable of handling a wider range of tasks. Furthermore, significant attention is being paid to developing more efficient data collection methods and improving the safety and reliability of these systems.
Papers
Task-oriented Robotic Manipulation with Vision Language Models
Nurhan Bulus Guran, Hanchi Ren, Jingjing Deng, Xianghua Xie
MSGField: A Unified Scene Representation Integrating Motion, Semantics, and Geometry for Robotic Manipulation
Yu Sheng, Runfeng Lin, Lidian Wang, Quecheng Qiu, YanYong Zhang, Yu Zhang, Bei Hua, Jianmin Ji
LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation
Zhijie Wang, Zhehua Zhou, Jiayang Song, Yuheng Huang, Zhan Shu, Lei Ma
Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation
Paul Jansonnie, Bingbing Wu, Julien Perez, Jan Peters
A Compact, Low-cost Force and Torque Sensor for Robot Fingers with LED-based Displacement Sensing
Amr El-Azizi, Sharfin Islam, Pedro Piacenza, Ioannis Kymissis, Matei Ciocarlie
Autoregressive Action Sequence Learning for Robotic Manipulation
Xinyu Zhang, Yuhan Liu, Haonan Chang, Liam Schramm, Abdeslam Boularias
UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models
Qiaojun Yu, Siyuan Huang, Xibin Yuan, Zhengkai Jiang, Ce Hao, Xin Li, Haonan Chang, Junbo Wang, Liu Liu, Hongsheng Li, Peng Gao, Cewu Lu
Towards Effective Utilization of Mixed-Quality Demonstrations in Robotic Manipulation via Segment-Level Selection and Optimization
Jingjing Chen, Hongjie Fang, Hao-Shu Fang, Cewu Lu