最近有社招的同学面到了HR环节，最终因为变现不是很出色被筛下来了，很可惜！今天，我们不谈技术，就 分享下在面试过程中，HR这个环节应该怎么面？ HR最想考察的是什么？ 我们沟通下来，HR最看重的无外乎以下几点： hr 最想要的人就是：稳定，忠诚，容易合作，善于沟通！ 态度良好，负责。 HR面试常问的问题有哪些？ 1）沟通，综合能力判断： 请做一个简单的自我介绍。关键点：谦逊，自信，建议总分结构，逻辑清晰，优势突出。 介绍一下你的优点和缺点。 关键点： 真诚，谦虚，不要过多，褒义中带贬义，沟通上还需加强，技术上爱钻 牛角尖等。 2）稳定性类问题： 你为什么离开上家公司。关键点： 不说不稳定，不要仇视上家公司，从客观的原因分析，最好是被动的。 找工作看中的点。关键点： 往应聘公司特点上靠，成长，机会。 为什么要来我们公司。关键点：结合招聘公司的实际情况，成长收获，看好贵司。 3）沟通&态度类问题： 如果和主管有冲突或者意见，当如何处理？关键点：可以多从自己身上找原因，每个人看问题视角不一样，主 管可能更多注整体和全局。 1）稳定性：工作稳定，工作负责（不要1年一次跳槽，你就是能力再强，也不敢要） 2）思维上：逻辑 ...

Core Viewpoint - The article discusses job opportunities in the fields of multimodal large models, reinforcement learning, and navigation, highlighting positions in a unicorn company with ample funding [1]. Group 1: Multimodal Large Models - Job locations are in Beijing and Shenzhen with a salary range of 40k-80k/month [2]. - Responsibilities include developing cutting-edge algorithms for embodied intelligent multimodal large models applicable in various indoor and outdoor scenarios, focusing on framework design, model optimization, and training for navigation and operation tasks [2]. - Candidates should have a master's degree or higher in computer science, artificial intelligence, robotics, or control engineering, along with extensive experience in robot perception, navigation, and AI large models [3]. - Preferred qualifications include experience with algorithms related to multimodal large models in robot navigation and a solid foundation in algorithm development and engineering implementation [3][4]. Group 2: Reinforcement Learning - Job location is in Beijing with a salary range of 40k-80k/month [5]. - Specific job descriptions and requirements are not detailed in the provided text [5]. Group 3: Embodied Navigation Algorithms - Job location is in Shenzhen with a salary range of 30k-60k/month [6]. - The role involves researching and developing algorithms for embodied intelligence, focusing on the integration of multimodal data into planning and achieving end-to-end mapping from data to actions [6]. Group 4: Additional Qualifications - Candidates should have a strong foundation in machine learning, deep learning, and reinforcement learning, with the ability to conduct independent research in embodied intelligence and related fields [7]. - Experience in publishing papers in top conferences and journals is a plus, along with strong coding skills and participation in robotics competitions [7].

Core Viewpoint - The discussion focuses on the concept of remote operation (遥操作) in the context of embodied intelligence, exploring its significance, advancements, and future potential in robotics and human-machine interaction [2][15][66]. Group 1: Definition and Importance of Remote Operation - Remote operation is not a new concept; it has historical roots in military and aerospace applications, but its relevance has surged with the rise of embodied intelligence [5][15]. - The emergence of embodied intelligence has made remote operation crucial for data collection and human-robot interaction, transforming it into a mainstream approach [17][66]. - The concept of remote operation is evolving, with discussions on how it can enhance human capabilities and provide a more intuitive interface for controlling robots [15][66]. Group 2: Experiences and Challenges in Remote Operation - Various types of remote operation experiences were shared, including surgical robots and remote-controlled excavators, highlighting the diversity of applications [6][21]. - The challenges of remote operation include latency issues, the complexity of control, and the need for intuitive human-machine interfaces [34][69]. - The discussion emphasized the importance of minimizing latency in remote operation systems to enhance user experience and operational efficiency [34][56]. Group 3: Future Directions and Innovations - The future of remote operation may involve a combination of virtual and physical solutions, such as using exoskeletons for realistic feedback and pure visual systems for ease of use [38][40]. - Innovations like the ALOHA system are prompting the industry to rethink robot design and operational frameworks, potentially leading to significant advancements in remote operation technology [103][106]. - The integration of brain-machine interfaces could represent the ultimate solution for overcoming current limitations in remote operation, allowing for seamless communication between humans and machines [37][99].

Core Viewpoint - MagicLab's new bipedal humanoid robot, MagicBot Z1, redefines the value of humanoid robots through a combination of high-performance hardware, an open AI ecosystem, and diverse application scenarios [1][15]. Group 1: Product Features - MagicBot Z1 features a self-developed high-performance joint module with 24 basic degrees of freedom, expandable to 49 degrees, and a maximum joint torque exceeding 130 N.m, enabling advanced movements like "large disturbance impact recovery" and "continuous getting up" [1]. - The robot is constructed from high-strength aluminum alloy and engineering plastics, enhancing its durability and resistance to falls and wear, ensuring stable performance even after impacts [5]. - Equipped with a variety of sensors including 3D LiDAR, depth cameras, and stereo cameras, MagicBot Z1 can autonomously navigate complex environments, making it suitable for applications in family companionship, commercial explanations, and specialized uses [7]. Group 2: Developer Support - MagicLab provides a developer platform that allows developers to master new actions in just 20 minutes, accelerating training speed and enhancing the humanoid nature of movements [9]. - The robot's robust motion control algorithms enable it to adapt to various terrains, ensuring rapid deployment in different scenarios [9]. Group 3: Interaction Capabilities - MagicBot Z1 features human-like emotional interaction capabilities, utilizing rich visual and tactile perception to create a multi-modal interaction system, transforming the robot from a mere tool into a life companion [13]. Group 4: Market Positioning - As a comprehensive embodiment of world perception, full-body movement, dexterous operation, and physical interaction, MagicBot Z1 sets a new benchmark in the humanoid robot industry, offering high-value solutions for research, education, commercial services, industrial operations, and family companionship [15]. - The product line includes a standard version for enthusiasts and a developer version that allows customization for various applications, promoting innovation and exploration in different industry scenarios [15]. Group 5: Company Background - MagicLab is a leading company in embodied intelligence technology, focusing on the research, production, and industrial application of humanoid robots, with a self-developed hardware and software stack [16]. - The company has completed two rounds of financing totaling over 250 million yuan within six months, with funds primarily allocated for research and development of embodied intelligence technology and industrial/commercial applications [17].

Core Viewpoint - The article emphasizes the rapid evolution and significance of Vision-Language-Action (VLA) models in the field of embodied intelligence, highlighting their potential to revolutionize human-robot interaction and the robotics industry as a whole [4][6][17]. Group 1: VLA Model Development - VLA models are becoming the core driving force in embodied intelligence, gaining traction among researchers and companies globally [7][8]. - Google recently released the first offline VLA model, enabling robots to perform tasks without internet connectivity [9]. - The emergence of the Fast-in-Slow (FiS-VLA) model in China represents a significant advancement, integrating fast and slow systems to enhance robotic control efficiency and reasoning capabilities [10][12]. Group 2: Academic and Industry Trends - There has been an explosive growth in academic papers related to VLA, with 1,390 papers published this year alone, accounting for nearly half of all related research [14]. - The VLA technology is facilitating the transition of robots from laboratory settings to real-world applications, indicating its vast potential [16][17]. Group 3: Key Innovations and Breakthroughs - The RT-2 model from Google marked a pivotal moment in VLA development, introducing a unified model architecture that integrates visual, language, and action modalities [38][40]. - The RoboMamba model, developed in China, significantly improved efficiency and reasoning capabilities in VLA models, achieving a threefold increase in inference speed compared to mainstream models [52][48]. - OpenVLA, another significant model, demonstrated superior performance in various tasks while being more efficient than previous models, achieving a 16.5% higher success rate than RT-2 [57][58]. Group 4: Future Directions and Implications - The introduction of the π series models aims to enhance VLA's generalization capabilities, allowing robots to perform complex tasks with minimal training [62][70]. - The FiS-VLA model represents a breakthrough in real-time control, achieving an 11% improvement in success rates in real environments compared to existing methods [114]. - The advancements in VLA technology are paving the way for robots to operate effectively in diverse environments, marking a significant step towards achieving Artificial General Intelligence (AGI) [127][123].

Group 1 - The core viewpoint of the article highlights the acquisition of 63.62% of shares in Shangwei New Materials by Zhiyuan Robotics, marking a significant move in the robotics industry and potentially becoming a landmark merger case in the A-share market [1] - Zhiyuan Robotics has developed three major robot families, with expected shipments reaching thousands by 2025, indicating strong growth potential in various commercial applications [1] - The acquisition is positioned as a notable event following the release of the "National Nine Articles" and "Merger Six Articles," emphasizing the importance of new productivity enterprises in the A-share market [1] Group 2 - The domestic robotics sector has seen a surge in activity this year, with frequent financing and IPO announcements, indicating a vibrant investment landscape [2] - Starry Sky Technology has successfully completed two rounds of strategic financing, raising over $100 million, showcasing the increasing investor interest in robotics [3]

Core Insights - The article discusses advancements in Vision-Language-Action (VLA) models through reinforcement learning (RL) techniques, specifically the Proximal Policy Optimization (PPO) algorithm, which significantly enhances the generalization capabilities of these models [2][4]. Group 1: VLA Model Enhancements - The application of PPO has led to a 42.6% increase in task success rates in out-of-distribution (OOD) scenarios [2]. - Semantic understanding success rates improved from 61.5% to 75.0% when encountering unseen objects [2]. - In dynamic interference scenarios, success rates surged from 28.6% to 74.5% [2]. Group 2: Research Contributions - A rigorous benchmark was established to evaluate the impact of VLA fine-tuning methods on generalization across visual, semantic, and execution dimensions [4]. - PPO was identified as superior to other RL algorithms like GRPO and DPO for VLA fine-tuning, with discussions on adapting these algorithms to meet the unique needs of VLA [4]. - An efficient PPO-based fine-tuning scheme was developed, utilizing a shared actor-critic backbone network, VLA model preheating, and minimal PPO training iterations [4]. - The study demonstrated that RL's generalization capabilities in VLA for semantic understanding and entity execution outperformed supervised fine-tuning (SFT), while maintaining comparable visual robustness [4]. Group 3: NavMorph Model - The NavMorph model was introduced as a self-evolving world model for vision-and-language navigation in continuous environments, achieving a success rate of 47.9% in unseen environments [13][15]. - The model incorporates a World-aware Navigator for inferring dynamic representations of the environment and a Foresight Action Planner for optimizing navigation strategies through predictive modeling [15]. - Experiments on mainstream VLN-CE benchmark datasets showed that NavMorph significantly enhanced the performance of leading models, validating its advantages in adaptability and generalization [15].

Core Viewpoint - The industry of embodied intelligence is evolving, with a focus on humanoid robots and their deployment challenges, particularly in terms of stability and maintenance costs [1][2]. Group 1: Humanoid Robots and Deployment - Humanoid robots are expected to be the most popular in 2025, but their deployment is hindered by stability issues, which could lead to high repair costs and unclear liability [1]. - Compared to humanoid robots, mobile operations combined with robotic arms are more likely to achieve practical applications, as demonstrated by products like Galaxy General's G1 in service and retail environments [1]. Group 2: Data and Model Training - A large-scale dataset is essential for pre-training foundational models, with the efficiency and quality of data collection being critical for scaling applications [4]. - The sim2real approach addresses challenges related to data scarcity and cost, but ensuring performance in real-world scenarios remains a significant focus area [4]. Group 3: Community and Resources - The "Embodied Intelligence Heart Knowledge Planet" community offers a platform for technical exchange among nearly 200 companies and research institutions in the field [5][12]. - The community provides resources for newcomers, including technical stacks, project proposals, and job opportunities, fostering a comprehensive ecosystem for embodied intelligence [9][11][16]. Group 4: Educational and Research Support - The community has compiled a wealth of resources, including open-source projects, datasets, and learning pathways across various aspects of embodied intelligence [18][31][33]. - Members can access a variety of educational materials, including research papers and technical documentation, to support their learning and development in the field [20][23][25].

Core Viewpoint - The article discusses the limitations of foot robots in real-world applications due to the gap between simulation and reality, particularly in high-level tasks requiring RGB perception. It introduces a "Real-Sim-Real" framework that enhances visual navigation and motion control through a digital twin simulation environment [2]. Group 1 - The movement control of foot robots benefits from the combination of reinforcement learning and physical simulation, but is hindered by the lack of realistic visual rendering [2]. - The proposed "Real-Sim-Real" framework utilizes multi-view images for 3D Gaussian splatting (3DGS) scene reconstruction, creating a simulation environment that combines photo-realism with physical interaction characteristics [2]. - Experiments in the simulator demonstrate that the method supports the transfer of reinforcement learning strategies from simulation to reality using pure RGB input, facilitating rapid adaptation and efficient exploration in new environments [2]. Group 2 - The framework shows potential applications in home and factory settings, indicating its relevance for practical deployment in various environments [2]. - The paper titled "VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and Locomotion" is linked for further reading [3]. - Additional project details can be found on the provided project link [3].

星动纪元再获5亿融资！团队大牛伯克利和清华交叉信息研究背景 2025年7月7日，"清华系"人形机器人创企创企【北京星动纪元科技有限公司】（以下简称"星动纪 元"）完成5亿元A轮融资，本轮融资由鼎晖资本和海尔资本联合领投，厚雪资本、华映资本、襄禾资 本、丰立智能等财务机构及产业资本跟投，老股东清流资本、清控基金等机构继续追加投资。 本轮融资将用于人形机器人软硬技术的研发与量产落地，推动"模型-本体-场景数据"闭环飞轮高速运 转。 公司介绍 北京星动纪元科技有限公司成立于2023年8月，由清华大学交叉信息研究院孵化，也是唯一一家清华 大学占股的人形机器人企业。创始人陈建宇是清华大学博士生导师、助理教授。公司最初由图灵奖 得主、中科院院士、清华大学交叉信息研究院院长姚期智院士团队和上海期智研究院孵化；星动纪 元不少技术都是由清华大学交叉信息学院的技术成果转化而来。 陈建宇：清华大学交叉信息研究院助理教授、博士生导师；清华大学本科、UC Berkeley博士、师从 美国工程院院士、机电控制专家、MPC算法理论奠基人Masayoshi Tomizuka教授，同时也是清华大学 特聘研究员、由姚期智亲自招募，拥有10+年机 ...