Core Viewpoint - The article discusses various advanced full-body motion capture solutions, highlighting their technical complexities and potential applications in robotics and teleoperation [1][3]. Group 1: OpenWBC Project - OpenWBC enables full-body control of the Unitree G1 robot using Apple Vision Pro for upper body teleoperation and OpenHomie algorithm for lower body movement, supporting full-body data collection [3][5]. Group 2: TWIST Project - TWIST, developed by Stanford University, represents a significant advancement in humanoid robot teleoperation, utilizing full-body motion imitation to achieve coordinated actions through a single neural network controller [6][11]. Group 3: AMO Project - The Adaptive Motion Optimization (AMO) framework from UC San Diego combines reinforcement learning and trajectory optimization for real-time adaptive full-body control, demonstrating superior stability and expanded workspace [9][11]. Group 4: R²S² Framework - The Real-world-Ready Skill Space (R²S²) framework focuses on developing a skill library for humanoid robots, ensuring optimal performance and robust transferability from simulation to real-world tasks [16]. Group 5: CLONE Project - The CLONE system from Beijing Institute of Technology addresses the challenges of humanoid robot teleoperation by implementing a closed-loop correction system, achieving high fidelity in full-body operations with minimal drift [20]. Group 6: Community and Resources - The article promotes the "Embodied Intelligence Knowledge Planet," a community for sharing cutting-edge academic content, open-source projects, and job opportunities in the field of embodied intelligence [23][25][32].

最近很多同学咨询我们全身动捕数据的方案，相比于遥操作/VR+动捕手套，这种方案技术难度上更大，今 天也为大家汇总几篇行业里面比较知名的全身动捕方案。更多内容欢迎移步到具身智能之心知识星球，一 个交流技术和方案的地方。 OpenWBC 项目链接：https://github.com/jiachengliu3/WBC_Deploy 本项目实现了对 Unitree G1 机器人的全身控制：使用 Apple Vision Pro 结合 avp_teleoperate 控制机器人上 半身，使用 OpenHomie 算法控制下半身运动。同时支持 全身数据采集 功能。 主要功能特性： TWIST 双模式控制 : 上半身远程操控 + 下半身自主行走 实时控制 : 基于 Apple Vision Pro 的低延迟控制 全身数据采集 : 支持完整的机器人动作数据收集 模块化设计 : 可独立部署上半身或下半身控制 跨平台通信 : TCP/IP 网络通信架构 TWIST: Teleoperated Whole-Body Imitation System 项目链接：https://yanjieze.com/TWIST/ 斯坦福大学团队 ...

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Jianqiang Xiao等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 目标导航（ObjectNav）作为一种替代方案，要求智能体基于语义线索定位目标，无需密集的指令序列。但现有ObjectNav研究主要集中在地面室内场景，在大规 模、非结构化的户外空中环境中仍未得到充分探索。为此，UAV-ON基准被提出，旨在推动无人机在复杂现实环境中基于语义目标描述的自主导航研究。 UAV-ON基准概述 UAV-ON是首个针对无人机在开放世界中进行实例级目标导航的大规模基准，其核心特点包括： 多样化环境 ：包含14个基于Unreal Engine构建的高保真户外环境，覆盖城市、森林、山地、水域等多种场景，空间尺度从350×250到1400×1250单位不等，总 水平面积约900万平方单位，体现了真实世界的语义丰富性和视觉复杂性。 语义目标设计 ：定义了1270个标注目标，每个目标对应一个实例级 ...

不造硬件、不量产、不做商业化。 这是腾讯加入当下具身智能热潮的姿势。 那要做什么？ 一个具身智能的 通用外接大脑 。而且不是端到端，是 模块化 提供能力。也就是各家机器人可以从中获取自己想要的部分能力。 效果be like，搭载了该大脑的宇树机器人，可以实时处理人类语音指令，闲聊、完成任务，还能判断自己能干什么、不能干什么。 比如它能看到桌子上比原先多了一个玩偶，但是它没有灵巧手，所以并不能拿起玩偶。 编辑丨 量子位 点击下方 卡片 ，关注" 具身智能之心 "公众号 >> 点击进入→ 具身 智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 这就是具身智能Tarios平台，在WAIC 2025期间正式亮相。 它集成了目前腾讯在具身智能领域的软件能力，包括多模态、规划、感知算法，以及开发、仿真、数据等工具。 包括宇树、越疆、乐聚、帕西尼、擎朗、众擎等大热具身智能领域玩家，都已火速达成合作。 而且还没啥后顾之忧——腾讯再次强调了自己不下场做硬件本体、不搞量产、不搞商业化。 这一波直接格局打开，谁都能来当"腾讯系"机器人了（doge） ...

无界智慧（Spatialtemporal AI）是一家专注于时空智能与具身智能融合创新的前沿科技企业。我们以自主研 发的时空智能技术为核心，致力于构建新一代具备多模态感知、自主认知与决策、精准任务执行能力的智 能体系统。 团队成员来自 PKU、清华、CASIA、CMU、MBZUAI 等高校及研究机构的教授与博士生。面向康养与医 疗场景，我们将打造"成长型数字家人"陪护机器人，深度融合时空感知、环境理解与行为决策，为养老 院、康养社区、居家及医疗照护机构提供可商用、可扩展的 AI 伙伴。 欢迎将简历投递到对应邮箱，将第一时间安排面试！！！ 招聘发于国内首个具身全栈技术社区：具身智能之心知识星球，更多内容欢迎扫码加入学习！ 具身智能之心知识星球，截止到目前已经完成了产业、学术、求职、问答交流等多个领域的闭环。几个运 营的小伙伴每天都在复盘，什么样的社区才是大家需要的？花拳绣腿的不行、华而不实的不行、没人交流 的也不行、找不到工作的更不行。 于是我们就给大家准备了学术领域最前沿的内容、大佬级别圆桌、开源的代码方案、最及时的求职信息...... 星球内部为大家梳理了近30+技术路线，无论你是要找benchmark、还 ...

Core Viewpoint - The article introduces the Interleave-VLA framework, which enhances robot manipulation by utilizing interleaved image-text instructions, demonstrating significant improvements in performance over existing models [2][3][7]. Group 1: Interleave-VLA Framework - Interleave-VLA is the first framework capable of understanding interleaved image-text instructions and generating continuous action sequences in the physical world [2]. - The framework is model-agnostic and requires minimal modifications to current state-of-the-art VLA models, providing strong zero-shot generalization capabilities [2][3]. Group 2: Data Set Development - A major challenge in implementing Interleave-VLA was the lack of a large-scale interleaved embodied dataset. To address this, an automated process was developed to convert pure text instructions from the Open X-Embodiment dataset into interleaved image-text instructions [2]. - The resulting dataset contains 210,000 interaction data points and 13 million frames of images, marking the first large-scale real-world interleaved embodied dataset [2]. Group 3: Performance Evaluation - Comprehensive evaluations in simulation benchmarks and real robot experiments show that Interleave-VLA significantly enhances cross-domain generalization capabilities by 2-3 times compared to state-of-the-art baseline models [3]. - The framework supports flexible task interfaces and can handle various user-provided image instructions, such as hand-drawn sketches, in a zero-shot manner [3]. Group 4: Advantages of Interleaved Instructions - The interleaved instruction paradigm effectively utilizes heterogeneous datasets and diverse instruction images, including those sourced from the internet, showcasing its substantial scalability potential [3][7].

无界智慧（Spatialtemporal AI）是一家专注于时空智能与具身智能融合创新的前沿科技企业。我们以自主研 发的时空智能技术为核心，致力于构建新一代具备多模态感知、自主认知与决策、精准任务执行能力的智 能体系统。 团队成员来自 PKU、清华、CASIA、CMU、MBZUAI 等高校及研究机构的教授与博士生。面向康养与医 疗场景，我们将打造"成长型数字家人"陪护机器人，深度融合时空感知、环境理解与行为决策，为养老 院、康养社区、居家及医疗照护机构提供可商用、可扩展的 AI 伙伴。 欢迎将简历投递到对应邮箱，将第一时间安排面试！！！ 招聘发于国内首个具身全栈技术社区：具身智能之心知识星球，更多内容欢迎扫码加入学习！ 具身智能之心知识星球，截止到目前已经完成了产业、学术、求职、问答交流等多个领域的闭环。几个运 营的小伙伴每天都在复盘，什么样的社区才是大家需要的？花拳绣腿的不行、华而不实的不行、没人交流 的也不行、找不到工作的更不行。 于是我们就给大家准备了学术领域最前沿的内容、大佬级别圆桌、开源的代码方案、最及时的求职信息...... 星球内部为大家梳理了近30+技术路线，无论你是要找benchmark、还 ...

点击下方 卡片 ，关注" 具身智能 之心 "公众号 作者丨 Dongming Wu等 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 Paper: https://arxiv.org/abs/2507.23734 写在前面：为什么"通用抓取"如此困难？ "让机器人学会抓取物体"一直是机器人领域的重要研究课题。然而，当我们把机器人从实验室的固定台面放到真实的开放世界，例如厨房、仓库、甚至马路边， 就会发现老的范式迅速失效： 一言以蔽之，机器人必须同时具备"功能推理+精细操作"两大能力。 近期，来自香港中文大学、原力灵机、中科院计算所、默罕默德·本·扎耶德人工智能大学、澳门大学等机构的研究者联合发布了全新Affordance Segmentation数据 集与模型框架，RAGNet和 AffordanceNet，旨在实现与人类指令对齐的通用抓取机器人。 二、RAGNet：面向通用抓取的大规模推理型数据集 RAGNet是一个大规模、基于推理的可供性（a ...

Group 1 - The article announces the establishment of a community focused on reinforcement learning, specifically targeting individuals working on quadrupedal, humanoid, and robotic arm control [1] - The community aims to create a platform for technical exchange and sharing within the industry [1] Group 2 - Interested individuals are encouraged to add a designated assistant on WeChat to join the group, with specific instructions for joining [2]

Core Viewpoint - The article discusses the development of AGENTSAFE, the world's first comprehensive evaluation benchmark for the safety of embodied intelligent agents, addressing the emerging risks associated with "jailbreak" attacks that can lead to dangerous actions by robots [5][12][43]. Group 1: Introduction to AGENTSAFE - AGENTSAFE is designed to fill the gap in adversarial safety evaluation for embodied agents, which have been largely overlooked in existing benchmarks [5][11]. - The research has received recognition, winning the Outstanding Paper Award at the ICML 2025 Multi-Agent Systems workshop [6]. Group 2: The Need for AGENTSAFE - Traditional AI safety concerns have focused on generating harmful content, while embodied agents can perform physical actions that pose real-world risks [10]. - The article emphasizes the importance of proactive safety measures, stating that safety vulnerabilities should be identified before any harm occurs [12]. Group 3: Features of AGENTSAFE - AGENTSAFE includes a highly realistic interactive sandbox environment, simulating 45 real indoor scenes with 104 interactive objects [14][15]. - A dataset of 9,900 dangerous commands has been created, inspired by Asimov's "Three Laws of Robotics," and includes six advanced "jailbreak" attack methods [16][20]. Group 4: Evaluation Methodology - AGENTSAFE employs an end-to-end evaluation design that assesses the entire process from perception to action execution, ensuring a comprehensive safety assessment [21][23]. - The evaluation is structured into three stages: perception, planning, and execution, with a focus on the model's ability to translate natural language commands into executable actions [31]. Group 5: Experimental Results - The study tested five mainstream vision-language models (VLMs), revealing significant performance disparities when faced with dangerous commands [30][34]. - For example, GPT-4o and GLM showed high refusal rates for harmful commands, while Qwen and Gemini had much lower refusal rates, indicating a higher susceptibility to dangerous actions [36][37]. - The results demonstrated that once commands were subjected to "jailbreak" attacks, the safety measures of all models significantly deteriorated, with GPT-4o's refusal rate dropping from 84.67% to 58.33% for harmful commands [39][43]. Group 6: Conclusion - The findings highlight the current vulnerabilities in the safety mechanisms of embodied intelligent agents, stressing the need for rigorous safety testing before deployment in real-world scenarios [43][44].