近日美国具身智能公司Physical Intelligence旗下的VLA模型π0.5正式开源，π0.5最核心的能力在于，通过异 构数据协同训练与多模态数据融合，基于优化的模型架构，实现了机器人在复杂现实场景下强大的泛化能力， 使其能理解任务语义、拆解复杂任务流程并精准执行动作。 ▍ π0.5开源模型都有哪些技术亮点？ π0.5 的一大技术亮点是采用了异构数据协同训练的方式。该模型整合了来自多个机器人、高级语义预测、网 络数据等多种不同来源的数据，通过协同训练，能够让模型能够实现更广泛的泛化，更好地适应现实世界中的 机器人操作任务。在训练过程中，模型不仅能学习到物理技能的执行方法，还能理解每个技能背后的语义背 景，推断任务的高级结构，甚至可以从其他机器人那里借鉴物理行为经验。 此外π0.5能够将图像观察、语言命令、目标检测、语义子任务预测和低级动作等多模态数据示例进行融合。 这些数据不是简单地叠加在一起，而是在训练中深度融合。比如，图像观察数据让机器人能识别环境中的物体 信息，语言命令数据帮助机器人理解人类意图，目标检测数据能让机器人快速锁定任务相关物体，语义子任务 预测数据可辅助规划任务流程，低级动作数据则 ...

Core Viewpoint - The article discusses the release of π0.5 and WALL-OSS, highlighting their advancements in embodied intelligence and the significance of these models in the robotics industry, particularly in enhancing task execution in complex environments [1][3][5]. Group 1: Model Capabilities - π0.5 demonstrates enhanced generalization capabilities through heterogeneous task collaborative training, enabling robots to perform long-term, fine-grained operations in new household environments [3][5]. - WALL-OSS achieves embodied perception through large-scale multimodal pre-training, allowing seamless integration of instruction reasoning, sub-goal decomposition, and fine-grained action synthesis within a single differentiable framework [8][18]. - The model exhibits high success rates in complex long-term manipulation tasks, showcasing robust instruction-following abilities and understanding of complex scenarios, surpassing existing baseline models [8][18][28]. Group 2: Training and Data - The training process for WALL-OSS involves discrete, continuous, and joint phases, requiring only RTX 4090-level computational power for training and inference deployment [14][15]. - A multi-source dataset centered on embodied tasks was constructed, addressing the lack of large-scale, aligned VLA supervision and current visual language models' spatial understanding gaps [20][22]. - The dataset includes thousands of hours of data, focusing on both short-range operation tasks and long-range reasoning tasks, ensuring comprehensive training for the model [20][22][24]. Group 3: Experimental Analysis - Experimental analysis on embodied visual question answering and six robotic operation tasks focused on language instruction understanding, reasoning, and generalization, as well as planning and execution of long-term, multi-stage tasks [25][31]. - WALL-OSS significantly outperformed its original baseline model in object grounding, scene captioning, and action planning tasks, demonstrating its enhanced scene understanding capabilities [27][28]. - The model's ability to follow novel instructions without task-specific fine-tuning was validated, achieving 85% average task progress on known object instructions and 61% on novel object instructions [29][31]. Group 4: Industry Impact - The advancements in WALL-OSS and π0.5 are positioned to address existing limitations in visual language models and embodied understanding, paving the way for more capable and versatile robotic systems [5][8][20]. - The company, established in December 2023, focuses on developing a general embodied intelligence model using real-world data, aiming to create robots with fine operational capabilities [39]. - The recent completion of a nearly 1 billion yuan A+ round of financing indicates strong investor confidence in the company's direction and potential impact on the industry [39].

Core Viewpoint - The article discusses the advancements in robotic models, particularly focusing on the development of the RT-2 and RT-X models, which enhance the capabilities of robots in executing tasks through visual language models and diverse datasets [5][10][11]. Group 1: RT-2 and Its Capabilities - RT-2 is introduced as a foundational robot model that can process visual questions and execute tasks based on language instructions, showcasing the potential of remote-accessible robotic models [5][7]. - The model's ability to convert robot control tasks into question-answer formats allows it to perform various basic language instructions effectively [7][8]. Group 2: RT-X Dataset and Its Impact - The RT-X dataset, developed by DeepMind, comprises data from 34 research labs and 22 types of robots, providing a diverse training ground for robotic models [10]. - Models trained on the RT-X dataset outperform specialized models by approximately 50% in various tasks, indicating the advantages of cross-embodiment models [11]. Group 3: Evolution of VLA Models - The first-generation VLA model, RT-2, is noted for its simplicity, while the second-generation models utilize continuous action distributions for improved performance in complex tasks [14][15]. - The second-generation VLA models incorporate specialized mechanisms for generating continuous actions, enhancing their control capabilities [17][18]. Group 4: π0 and π0.5 Models - The π0 model, based on a large language model with 3 billion parameters, is designed to handle various tasks, including folding clothes, demonstrating its adaptability in different environments [18][23]. - The latest π0.5 model is aimed at executing long-term tasks in new environments, integrating high-level reasoning capabilities to manage complex instructions [28][30]. Group 5: Future Directions and Reinforcement Learning - Future VLA models are expected to integrate reinforcement learning techniques to enhance robustness and performance, moving beyond imitation learning [34][39]. - The combination of VLA and DLA (Deep Learning Architecture) is proposed to create a more effective system, leveraging expert data to improve generalist capabilities [44][46].

Core Viewpoint - The article discusses the advancements in robotic models, particularly focusing on the development of the RT-2 and RT-X models, which enhance the capabilities of robots in executing complex tasks through improved data sets and model architectures [6][12][44]. Group 1: RT-2 and RT-X Models - RT-2 is introduced as a foundational robot model that utilizes a visual language model to process image-based commands and execute tasks [8][10]. - The RT-X dataset, developed by DeepMind, comprises data from 34 research labs and 22 types of robots, showcasing a diverse range of robotic capabilities [13][26]. - Cross-embodiment models trained on the RT-X dataset outperform specialized models by approximately 50% in various tasks, indicating the advantages of generalization in robotic learning [13][29]. Group 2: Evolution of VLA Models - The first generation of VLA models, like RT-2, is based on simple question-answer structures for robot control, while the second generation incorporates continuous action distributions for better performance [16][19]. - The second generation VLA models, such as π0, utilize a large language model with an action expert module to handle complex tasks, generating action sequences over time [22][24]. - The π0.5 model is designed for long-term tasks, integrating high-level reasoning to execute complex instructions in new environments [36][40]. Group 3: Integration of Reinforcement Learning - Future VLA models are expected to incorporate reinforcement learning techniques to enhance robustness and performance, moving beyond imitation learning [44][49]. - The integration of reinforcement learning with VLA aims to create a more effective training process, allowing robots to learn from both expert data and real-world interactions [56][60]. - Current research is focused on developing stable and effective end-to-end training processes that leverage reinforcement learning to improve VLA capabilities [60].

Core Insights - The development of humanoid robots for industrial applications faces significant challenges, particularly in the concept validation phase, which tests the engineering capabilities of teams [1][9][10] Group 1: VLA Model Development - Lingchu Intelligent recently launched the Psi-R1 model, a Vision-Language-Action (VLA) model, which aims to enable robots to perform complex tasks in open environments [2][4] - Since 2025, at least seven companies, including Physical Intelligence and NVIDIA, have released VLA-related models, indicating a growing interest in this technology [2][7] - The VLA model's ability to incorporate action signals as input is crucial for improving the robot's decision-making and operational capabilities [5][8] Group 2: Concept Validation Challenges - The concept validation phase requires humanoid robots to demonstrate technical success rates, reliability, efficiency, cost, and profitability, which are critical for commercial viability [3][10] - The transition from laboratory testing to real-world application involves multiple stages, including a three-month internal testing phase and a subsequent three-month validation phase in customer environments [12][13] - Real-world conditions, such as complex lighting and electromagnetic interference, pose additional challenges that must be addressed during the validation process [12][13] Group 3: Market Applications and Limitations - Current humanoid robots are primarily engaged in tasks such as material handling and inspection in various industrial settings, but their roles are often limited to simple operations [14][15] - Companies are focusing on scenarios where humanoid robots can perform tasks that are difficult for automated systems, such as quality inspection in 3C manufacturing [15] - The ultimate goal is for humanoid robots to take on roles that require flexibility and adaptability, which traditional automation cannot achieve [15]