他联合清华助理教授高阳、工业机器人出海先行者郑灵茵，于2024年创立千寻智能。 骨干成员多来自UC Berkeley、清华、北大等顶尖学府，平均年龄不到30岁，已在多模态大模型、机器人学、强化学习等具身模型核心领域，有较深厚的 学术与工程基因。 团队自研具身大脑模型，打造轮式底盘加人形上身的"墨子机器人"。 2025年底，"小墨"正式投入宁德时代中州基地的新能源动力电池PACK生产线，负责电池接插件插接等复杂作业，稳定量产。 马年伊始，具身智能赛道又迎来一位百亿玩家。 2月24日，千寻智能宣布，近期连续完成两轮融资，累计金额近20亿元，估值突破100亿元大关。 本轮融资获得云锋基金、红杉中国等机构，以及TCL创投、重庆产业母基金等产业和国有资本加持，顺为资本、达晨财智等老股东大额追投。 创始人兼CEO韩峰涛，现年42岁，本科毕业于华中科技大学自动化学院，博士师从机器人学术泰斗丁汉院士，深耕工业机器人行业十余年。 2014年，他从国企辞职，与同事联合创办珞石机器人，主导交付了超2万台工业机器人，覆盖20多个行业场景。 再次创业，韩峰涛定下目标，"十年让全球10%的人拥有自己的机器人"。 据RoboChallen ...

Core Viewpoint - The research on nanorobots for cancer treatment is advancing, with significant improvements in their ability to deliver drugs precisely to targeted areas within the body, potentially revolutionizing cancer diagnosis, monitoring, and treatment methods [2][3]. Group 1: Nanorobots Research - The team led by Sylvain Martel at the Montreal Institute of Technology has spent over a decade developing nanorobots that can be controlled using MRI technology to deliver cancer drugs directly to tumors [2][3]. - Recent experiments showed that by adjusting the position of pigs in MRI machines, the number of nanorobots reaching the liver increased by nearly three times compared to previous tests [2]. - The research indicates that the technology is nearing practical application in humans, which could lead to innovative methods for cancer treatment, including targeted drug delivery and tumor monitoring [3]. Group 2: Publication and Future Directions - The findings are published in a recent issue of Nature Focus, which explores the application of nanorobots in cancer treatment and other advanced robotics topics [4]. - Future editions of Nature Focus will cover rapidly developing topics in various fields, including robotics and AI, indicating a growing interest and investment in these technologies [5].

Core Viewpoint - The article emphasizes the importance of interdisciplinary research in embodied intelligence, highlighting opportunities for doctoral and postdoctoral candidates in deep learning and artificial intelligence, with a focus on high-level research platforms and international collaboration [2][10]. Research Content - Research directions include deep learning and artificial intelligence theories and algorithms [2]. - Candidates are expected to have a strong understanding and interest in core research areas, with the ability to conduct independent theoretical innovation and experimental validation [8]. Candidate Requirements - Candidates should possess relevant degrees in computer science, data science, automation, applied mathematics, or artificial intelligence from reputable institutions [8]. - Experience in publishing research in top international journals or conferences is preferred, showcasing strong research potential [9]. Skills and Qualifications - Familiarity with multimodal large models such as CLIP, BLIP, and LLaVA is essential [3]. - Proficiency in classic models like VAE, Transformer, and BERT, along with strong algorithm design and programming skills, particularly in high-performance languages like C++ or Rust, is advantageous [4][5]. - Understanding of large language model architectures and practical experience in unsupervised pre-training, SFT, and RLHF is a plus [6]. Professor's Profile - Professor Ji Xiaoqiang, with a PhD from Columbia University, leads a research lab focused on intelligent control systems and has published over 50 papers in top-tier journals and conferences [10]. - The lab aims to integrate control theory, artificial intelligence, robotics, high-performance computing, and big data for foundational and original research in intelligent systems [11]. Benefits and Compensation - Postdoctoral candidates may receive a pre-tax living allowance of 210,000 CNY per year, with additional university and mentor-specific compensation [12]. - Doctoral students can receive full or half scholarships covering tuition and living stipends, with top candidates eligible for a principal's scholarship [13]. - Research master's students have opportunities to transition to PhD programs and may receive additional living stipends [14]. Application Materials - Applicants must submit a complete CV in both Chinese and English, along with any published papers and materials demonstrating their research capabilities [15].

Core Insights - The article highlights the recruitment of graduate students for the Robotics Cognition and Learning (RCL) lab led by Dr. Xingxing Zuo at MBZUAI, focusing on various advanced fields in robotics and artificial intelligence [1][2]. Recruitment Focus - The lab is looking for candidates in areas such as Robotics, 3D Computer Vision, Mixed Reality, State Estimation, Learning-based Visual-Inertial SLAM, Multi-sensor Fusion, Reinforcement Learning, VLN/VLA, Humanoid-Object Interaction, and Embodied AI [2]. Admission Requirements - Candidates should have a strong interest in robotics or AI, a solid mathematical foundation, programming skills, self-management, motivation, innovation, and a rigorous research attitude. PhD applicants are expected to have published in top-tier journals as primary authors, while experience in robotics competitions is a plus [3]. Benefits and Compensation - PhD students receive a scholarship of approximately 420,000 RMB per year, tax-free, along with free round-trip airfare, ample GPU computing resources, and sufficient hardware resources for robotics and sensors [3]. Hardware Acquisition - The RCL lab has ordered various robotics hardware, with some already in use and the majority expected to arrive by November 2025 [4]. Application Deadlines - For Fall 2026 admissions, the application system opens on September 1, 2025, with an early deadline of November 15 and a late deadline of December 15. Applications for visiting researchers and domestic interns can be submitted year-round [6]. Application Process - Interested candidates must send an English resume, transcripts, and representative papers to Dr. Zuo's email, and also submit complete application materials through the official university application system [7]. Institutional Background - MBZUAI is recognized as the world's first university dedicated to artificial intelligence, recently admitting 115 undergraduate students with an acceptance rate of approximately 5% [9].

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]