作者丨 WenchengYe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。 研究背景与核心问题 Vision-Language-Action（VLA）模型在机器人操作、视觉导航等具身智能场景中表现出强大的多模态推理与动作预测能力，但庞大的架构、频繁的跨模态交互导致 计算开销大、推理延迟高，难以部署在实时或资源受限的机器人系统中。 现有高效VLA策略（如token剪枝、早期退出、轻量化架构）多沿用视觉-语言模型（VLM）的优化思路，优先基于视觉-语言相关性压缩模型，却忽略了动作预测的 核心目标，容易造成两大问题：一是关键信息损耗，感知和语义线索在压缩中被削弱，影响任务目标与环境上下文理解；二是动作语义不连贯，结构简化破坏了动 作相关语义的连续性，降低了动作策略的稳定性。 为解决这些问题，需要一种以动作预测为导向的高效蒸馏框架，在降低计算成本的同时，保留VLA模型的动作预测精度与稳定性。 核心方法：ActDistill 框架 ActDistill ...

Core Viewpoint - The exploration of Artificial General Intelligence (AGI) is increasingly focusing on embodied intelligence, which emphasizes the interaction and adaptation of intelligent agents within physical environments, enabling them to perceive, understand tasks, execute actions, and learn from feedback [1]. Industry Analysis - In the past two years, numerous star teams in the field of embodied intelligence have emerged, establishing valuable companies such as Xinghaitu, Galaxy General, and Zhujidongli, which are advancing the technology of embodied intelligence [3]. - Major domestic companies like Huawei, JD, Tencent, Ant Group, and Xiaomi are actively investing and collaborating to build a robust ecosystem for embodied intelligence, while international firms like Tesla and investment institutions are supporting companies like Wayve and Apptronik in the development of autonomous driving and warehouse robots [5]. Technological Evolution - The development of embodied intelligence has progressed through several stages: - The first stage focused on grasp pose detection, which struggled with complex tasks due to a lack of context modeling [6]. - The second stage involved behavior cloning, allowing robots to learn from expert demonstrations but revealing weaknesses in generalization and performance in multi-target scenarios [6]. - The third stage introduced Diffusion Policy methods, enhancing stability and generalization by modeling action sequences, followed by the Vision-Language-Action (VLA) model phase, which integrates visual perception, language understanding, and action generation [7][8]. - The fourth stage, starting in 2025, aims to integrate VLA models with reinforcement learning, world models, and tactile sensing to overcome current limitations [8]. Product and Market Development - The evolution of embodied intelligence technologies has led to the emergence of various products, including humanoid robots, robotic arms, and quadrupedal robots, serving industries such as manufacturing, home services, dining, and medical rehabilitation [9]. - The demand for engineering and system capabilities is increasing as the industry shifts from research to deployment, necessitating higher engineering skills for training and simulating strategies on platforms like Mujoco, IsaacGym, and Pybullet [23]. Educational Initiatives - A comprehensive curriculum has been developed to cover the entire technology route of embodied "brain + cerebellum," including practical applications and real-world projects, aimed at both beginners and advanced learners [10][20].

Core Viewpoint - The exploration of Artificial General Intelligence (AGI) is increasingly focusing on embodied intelligence, which emphasizes the interaction and adaptation of intelligent agents within physical environments, enabling them to perceive, understand tasks, execute actions, and learn from feedback [1]. Industry Analysis - In the past two years, numerous star teams in the field of embodied intelligence have emerged, leading to the establishment of valuable companies such as Xinghaitu, Galaxy General, and Zhujidongli, which are advancing the technology of embodied intelligence [3]. - Major domestic companies like Huawei, JD.com, Tencent, Ant Group, and Xiaomi are actively investing and collaborating to build a robust ecosystem for embodied intelligence, while international players like Tesla and investment firms are supporting companies like Wayve and Apptronik in the development of autonomous driving and warehouse robots [5]. Technological Evolution - The development of embodied intelligence has progressed through several stages: - The first stage focused on grasp pose detection, which struggled with complex tasks due to a lack of context modeling [6]. - The second stage involved behavior cloning, allowing robots to learn from expert demonstrations but revealing weaknesses in generalization and performance in multi-target scenarios [6]. - The third stage introduced Diffusion Policy methods, enhancing stability and generalization by modeling action sequences, followed by the emergence of Vision-Language-Action (VLA) models that integrate visual perception, language understanding, and action generation [7]. - The fourth stage, starting in 2025, aims to integrate VLA models with reinforcement learning, world models, and tactile sensing to overcome current limitations [8]. Product and Market Development - The evolution of embodied intelligence technologies has led to the emergence of various products, including humanoid robots, robotic arms, and quadrupedal robots, serving industries such as manufacturing, home services, dining, and medical rehabilitation [9]. - The demand for engineering and system capabilities is increasing as the industry shifts from research to deployment, necessitating training in platforms like Mujoco, IsaacGym, and Pybullet for strategy training and simulation testing [23]. Educational Initiatives - A comprehensive curriculum has been developed to cover the entire technology route of embodied "brain + cerebellum," including practical applications and advanced topics, aimed at both beginners and those seeking to deepen their knowledge [10][20].

Core Insights - The exploration towards Artificial General Intelligence (AGI) highlights embodied intelligence as a key direction, focusing on the interaction and adaptation of intelligent agents within physical environments [1] - The development of embodied intelligence is marked by the evolution of technology from low-level perception to high-level task understanding and generalization [6][9] Industry Analysis - In the past two years, numerous star teams in the field of embodied intelligence have emerged, establishing valuable companies such as Xinghaitu, Galaxy General, and Zhujidongli, transitioning from laboratories to commercial and industrial applications [3] - Major domestic companies like Huawei, JD, Tencent, Ant Group, and Xiaomi are actively investing and collaborating to build an ecosystem for embodied intelligence, while international players like Tesla and investment firms support advancements in autonomous driving and warehouse robotics [5] Technological Evolution - The evolution of embodied intelligence technology has progressed through several stages: - The first stage focused on grasp pose detection, which struggled with complex tasks due to a lack of context modeling [6] - The second stage involved behavior cloning, allowing robots to learn from expert demonstrations but revealing weaknesses in generalization and performance in multi-target scenarios [6] - The third stage introduced Diffusion Policy methods, enhancing stability and generalization through sequence modeling [7] - The fourth stage, emerging in 2025, explores the integration of VLA models with reinforcement learning and tactile sensing to overcome current limitations [8] Product Development and Market Growth - The advancements in embodied intelligence have led to the development of various products, including humanoid robots, robotic arms, and quadrupedal robots, serving industries such as manufacturing, home services, and healthcare [9] - The demand for engineering and system capabilities is increasing as the industry shifts from research to deployment, necessitating higher engineering skills [13] Educational Initiatives - A comprehensive curriculum has been developed to assist learners in mastering the full spectrum of embodied intelligence algorithms, covering topics from basic tasks to advanced models like VLA and its integrations [9][13]

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 今天自动驾驶之心为大家分享理想汽车最新的工作—DriveAction！ 探索VLA模型 中类人驾驶决策的基准。 如果您有相关工作需要分享，请在文末联系我们！ >>点击进入→ 自动驾驶之心 『多模态大模型』技术交流群 论文作者 | Yuhan Hao等 编辑 | 自动驾驶之心 研究背景与问题提出 在自动驾驶技术不断发展的进程中，Vision-Language-Action（VLA）模型凭借其强大的多模态处理能力， 为自动驾驶系统的发展带来了新的机遇。然而，现有的基准数据集在场景多样性、动作级标注的可靠性以 及与人类偏好一致的评估协议等方面存在明显不足，这严重制约了VLA模型的进一步发展和实际应用。 具体来看，现有基准数据集主要存在以下问题： DriveAction基准的核心创新 为解决上述问题，本文提出了DriveAction基准，这是首个专为VLA模型设计的动作驱动基准，具有以下三 大核心创新： 场景多样性不足 ：大多数基准数据集基于开源数据构建，来源单一，难以覆盖现实驾驶中的各种复杂 场景，如道路合并与出口 ...