Core Viewpoint - The article discusses the challenges and advancements in deploying VLA (Variable Length Attention) algorithms and Reinforcement Learning (RL) in robotics, focusing on improving performance and efficiency in real-world applications [3][4]. Group 1: VLA Architecture and Model Challenges - The article highlights the existing pain points in the architecture and models of VLA, indicating that there are still significant areas for improvement [4][8]. Group 2: Full-Body Motion Control for Robots - It explores the potential advancements in full-body motion control solutions for robots, emphasizing the need for better performance in tasks such as dancing [4][8]. Group 3: VLA and RL Integration - The discussion includes how to effectively integrate VLA with RL for real machine deployment, addressing the selection of hardware ("板子") and strategies for lightweight implementation [4][8]. Group 4: Expert Contributions - The article features insights from various experts in the field, including representatives from companies and academic institutions, who share their perspectives on the discussed topics [9][11][13]. Group 5: Additional Resources - It mentions that a more in-depth analysis and technical details are available on the "Embodied Intelligence Heart" knowledge platform, which includes exclusive content and Q&A sessions [19].

Core Viewpoint - The article discusses the challenges and advancements in deploying VLA (Variable Length Attention) algorithms and Reinforcement Learning (RL) in robotics, focusing on improving full-body motion control and real-world application [3][4][5]. Group 1: VLA Architecture and Challenges - The article highlights the pain points in the architecture and models of VLA, indicating that there are still significant challenges to overcome for effective implementation [4][8]. Group 2: Full-Body Motion Control - It explores the potential for evolution in full-body motion control solutions for robots, emphasizing the need for advancements to enhance performance [4][8]. Group 3: Real-World Deployment of VLA and RL - The discussion includes strategies for better real-world deployment of VLA combined with RL, addressing how to select appropriate hardware ("板子") and the importance of lightweight solutions [4][8].

Group 1 - The article discusses the transition of live streaming and content acquisition to the "Embodied Intelligence Heart Knowledge Planet" platform [2] - It highlights the high-quality roundtable discussions previously held on topics such as ontology, data, and simulation [2] - The focus of the current discussion is on the VLA algorithm and its implementation with reinforcement learning (RL) [3] Group 2 - Key topics include the pain points of the VLA architecture and model [6] - Exploration of advancements in full-body motion control solutions for robots to improve their performance [6] - Discussion on how to effectively implement VLA with RL on real machines, including board selection and lightweight design [6] Group 3 - The article features notable guests such as the Vice President of Algorithm at Digua Robotics, the Chief Researcher at Beijing Humanoid Robotics, and a partner at Yuanli Lingji [9][11][13] - It also mentions a Tsinghua University PhD who will soon join the Tsinghua Institute of Advanced Studies as an assistant professor [15] Group 4 - The article promotes the availability of in-depth content on the "Embodied Intelligence Heart" knowledge platform, including technical details, Q&A, and exclusive insights [18] - It emphasizes the importance of dexterous hand design as a key technology for closing the "hand-eye-brain" perception loop [18] - The article introduces the concept of "Agent" and its significance in thought, academia, and engineering [18] - It mentions the Spec-VLA framework designed for inference acceleration specific to VLA [18] - The latest developments from CMU on cross-entity world models aiding in small-sample robot learning are also highlighted [18]

Core Viewpoint - The article discusses the challenges and advancements in deploying VLA (Variable Latency Algorithm) and RL (Reinforcement Learning) in robotics, focusing on improving full-body motion control and real-world application [3][4][5]. Group 1: VLA Framework and Model Challenges - The article highlights the existing pain points in the VLA framework and model, indicating areas that require further development [4][8]. - It emphasizes the need for better integration of VLA with RL to enhance real-world applications and the selection of appropriate hardware [4][8]. Group 2: Advancements in Robotics Motion Control - The discussion includes potential improvements in full-body motion control for robots, aiming to enhance their performance in tasks such as dancing [4][8]. - The article suggests exploring lightweight solutions for VLA and RL implementations to optimize efficiency [4][8]. Group 3: Expert Contributions - The article features insights from various experts in the field, including representatives from Diguo Robotics, Beijing Humanoid Robotics, and Tsinghua University, who contribute to the discussion on VLA and RL [9][11][13]. - The event is hosted by a co-founder of "Embodied Intelligence Heart," indicating a collaborative effort in advancing robotics technology [15].

Core Viewpoint - Embodied AI is becoming a key force in advancing robotics from "executable" to "efficient excellence," addressing current research bottlenecks in hardware adaptability, high algorithm reproduction costs, and the disconnection in the "perception-decision-execution" chain [1][4][21]. Group 1: Research Bottlenecks - Current research teams face three main bottlenecks: insufficient hardware platform adaptability, high costs of algorithm reproduction, and the disconnection in the "perception-decision-execution" chain [1]. - The lack of general-purpose robots to meet the refined needs of multi-modal data collection is a significant challenge [1]. - The complexity of heterogeneous data processing and model training cycles adds pressure to research efforts [1]. Group 2: Technical Sharing Event - A recent technical sharing live stream titled "Frontier Practice of Embodied Intelligence" hosted by Shihe Robotics attracted over 7,000 viewers, focusing on the integration of advanced algorithms with robotic hardware [1][4]. - Dr. Hu systematically analyzed six categories of VLA (Vision-Language-Action) algorithms and demonstrated the reproduction of the RDT (Robotics Diffusion Transformer) model on real hardware [1][4]. Group 3: EA200 Robot Introduction - The EA200 robot, based on Shihe's years of expertise in mobile chassis and dual-arm collaboration, serves as a stable and comprehensive platform for embodied research [7]. - EA200 features a multi-dimensional perception input matrix, enhancing environmental understanding and human-robot interaction capabilities [9]. - The robot's 6-degree-of-freedom arm system supports high-load capabilities and complex dual-arm collaborative tasks, providing quality action execution and sample collection for models like RDT [9][15]. Group 4: Software and Computational Support - EA200 integrates the ROS2 navigation system and proprietary algorithms, supporting a full process from environment mapping to autonomous navigation, significantly reducing the complexity and cost of secondary development [11]. - The robot is equipped with external inference industrial computers and training servers to meet real-time response and large-scale training computational requirements [13]. - EA200 enables multi-modal data collection, model training optimization, and embedded inference deployment, effectively shortening the cycle from algorithm design to experimental validation [13][15]. Group 5: Market Positioning and Value Proposition - EA200 targets the robotics research and education market, providing a complete and user-friendly research support platform for universities, research institutes, and corporate R&D departments [16]. - The robot accelerates research rather than replacing it, standardizing key parameters to lower the threshold for algorithm reproduction and enhance model generalization [16]. - EA200 can simulate various real environments, supporting algorithm validation under different conditions, thus addressing the urgent need for standardized research platforms in embodied intelligence technology [16][18]. Group 6: Future Outlook - Embodied intelligence is positioned as a crucial direction for the evolution of AI and robotics, with VLA algorithms enabling robots to better understand human intentions and execute complex operations [19]. - Shihe Robotics aims to be an "enabler" in this breakthrough, allowing researchers to focus on algorithm innovation while minimizing hardware platform adaptation efforts [21]. - The launch of EA200 marks a significant transition for Shihe from a component supplier to a provider of integrated solutions, reflecting a deep understanding of market pain points and a strategic response to the growing demand for embodied intelligence [21].

Core Insights - The "Embodied Intelligence" forum during the 2025 World Artificial Intelligence Conference (WAIC) highlighted key challenges and advancements in the field of embodied intelligence, featuring prominent scholars from the Berkeley alumni group [1][2][4]. Group 1: Key Challenges in Embodied Intelligence - A significant bottleneck in embodied intelligence is the acquisition of high-quality data, which is essential for training robots to perform tasks effectively [4][9]. - The "data pyramid" model for embodied intelligence training emphasizes the need for diverse data sources, with the top tier consisting of remote operation data, the middle tier comprising human behavior data, and the base layer consisting of vast internet data [11][16]. - Current data collection methods are insufficient, as the largest publicly available dataset contains fewer than 1 million trajectories, which is significantly lower than the data available for language models [16]. Group 2: Future Vision and Development Stages - The envisioned future for robots includes three stages: integration into production systems, self-manufacturing capabilities, and assisting humans in expanding their capabilities, such as space exploration [5][6][8]. - The ultimate goal is to develop multi-agent systems where multiple embodied agents can interact and collaborate, enhancing their functionality and complexity [20][26]. Group 3: Data and Model Structure Enhancements - The introduction of "TactileVLA" aims to incorporate tactile feedback into the existing VLA model, improving robots' ability to perform tasks that require touch sensitivity [17]. - A new model called "OneTwoVLA" combines fast and slow thinking processes, allowing robots to analyze tasks and execute actions more effectively [18]. - The concept of "data scaling" is proposed, focusing on improving either world sampling or path sampling to enhance the training of embodied intelligence systems [34].

Core Viewpoint - The concept of "embodied intelligence" has been officially included in the 2025 government work report, highlighting its significance in current research by enterprises and educational institutions [1]. Group 1: Challenges in Implementation - Researchers and engineers face challenges when deploying algorithms from simulation environments to hardware, primarily due to insufficient engineering practice and a lack of thorough understanding of classic methods and imitation learning [2]. - These challenges hinder the effective integration of various methods, resulting in suboptimal deployment and performance of VLA algorithms on robotic arms, which obstructs the application of embodied intelligence in real-world scenarios [2]. Group 2: Training Program - Deep Blue Academy has partnered with notable figures and companies to launch an offline training camp focused on robotic arm operation and grasping, aimed at bridging the gap between simulation and real-world application [3]. - The training camp offers hands-on experience with real robotic arms and covers key technologies such as motion planning, visual feedback, imitation learning, and VLA, ensuring a comprehensive understanding of the "perception - decision - control" process [5]. Group 3: Course Highlights - The program emphasizes a full-stack technology loop, providing training from algorithms to hardware engineering capabilities [16]. - It features immersive project practice supported by the hardware platform of Songling Robotics, promoting deep integration of academia and industry resources [16]. - The course adopts a high-density small class format, ensuring intensive technical training and personalized guidance over three days [16]. Group 4: Target Audience - The training is designed for undergraduate and graduate students in robotics and automation-related fields, as well as R&D engineers in the field of robotic arms and embodied intelligence [18].