点击下方 卡片 ，关注" 具身智能 之心 "公众号 PI简介 左星星博士在MBZUAI 机器人系担任 Assistant Professor（Tenure-Track），领导Robotics Cognition and Learning (RCL)实验室。他曾在加州理工学院（Caltech）计算机与数学系 和 慕尼黑工业大学（TUM）计算 机系担任博士后，曾在Google公司全职担任Visiting Faculty Researcher。左博士的主要研究方向为机器人 学, 多模态SLAM, 3D场景理解, 具身智能和3D计算机视觉。在机器人和人工智能重要刊物T-RO、IJCV、J- FR、RA-L、ICRA、IROS、CVPR等发表论文四十余篇。受邀担任机器人领域著名期刊RA-L (2022年-至 今)，和机器人两大旗舰会议IROS（2022-2025年）， ICRA（2023-2026年）的Associate Editor。左博士 的长期研究目标致力于通过准确理解机器人的状态，周围3D环境以及动作执行，实现机器人和人类在开放环 境中的自然交互与无缝协作。 招生方向 Robotics, 3D Computer ...

Core Insights - The development of robotic dexterous hands is crucial for integrating robots into daily life, representing a significant engineering and scientific challenge [1][3] - Current robotic hands are inspired by human anatomy but still lack the full range of dexterity and functionality found in human hands [2][3] Group 1: Current State of Dexterous Hands - Robotic dexterous hands have evolved from simple end-effectors to more complex designs capable of multi-angle and multi-task operations, such as opening bottles and handling delicate objects [2] - The integration of tactile and force sensors allows robots to perceive object characteristics like shape and temperature, enhancing their operational capabilities [2][6] Group 2: Development Challenges - Achieving a fully functional robotic hand involves overcoming several challenges, including miniaturization, agility, and cost [7][8] - Increasing the degrees of freedom in robotic hands complicates the design and requires advanced integration techniques [7] - Current limitations in response speed and control algorithms hinder the dexterity of robotic hands, necessitating improvements in sensor technology and motor responsiveness [7][8] Group 3: Future Prospects - The path to making dexterous hands commercially viable includes optimizing design for mass production and balancing performance, cost, and reliability [8] - The ongoing training and data accumulation processes are essential for enhancing the dexterity and operational efficiency of robotic hands, akin to a child's learning process [8]

Group 1 - The article reviews seven selected papers published in the International Journal of Robotics Research, covering various research directions in robotics such as soft actuators, human-robot interaction, dual-arm robots, multi-robot systems, and bipedal locomotion control [1][6][18][27][38][48][58] Group 2 - A new low-profile soft rotary pneumatic actuator was designed, addressing the limitations of traditional soft pneumatic actuators in confined spaces and providing a compact solution for applications in wearable devices and biomedical devices [2][4] - The THÖR-MAGNI dataset was introduced to overcome data bottlenecks in social navigation and human-robot interaction, featuring multi-modal data collection and extensive scene coverage [6][11][14] - The FMB benchmark was developed to standardize robotic manipulation research, offering a diverse set of objects and a modular framework for imitation learning [18][20][24][26] - A framework for dual-arm robots to manipulate deformable linear objects in constrained environments was proposed, achieving high success rates in complex tasks [27][30][31][34] - A real-time planning method for large heterogeneous multi-robot systems was introduced, significantly improving computational efficiency and robustness in dynamic environments [38][40][45] - A survey on communicating robot learning during human-robot interaction highlighted the importance of a closed-loop communication framework to enhance collaboration [48][50][53][55] - Reinforcement learning was applied to bipedal locomotion control, demonstrating significant advancements in adaptability and robustness in complex environments [58][60][62]