具身机器人是通过本体结构、感知与决策构成的闭环在真实世界中自主行动并与环境交互的机器人系统，核心是在非结构化环境中通过学习与自适应完成多样化任务，能力包括本体设计、多模态感知、视觉-语言-动作模型与全身控制。它解决的是按预设轨迹执行的工业机器人无法应对环境变化的问题，并不解决灵巧操作与长时序任务的可靠性问题。
具身机器人的核心是感知与行动形成闭环，它的作用是：
- 让机器人在环境变化时重新决策。物体位置或形状改变后，感知结果被用于重新规划动作而非执行固定轨迹。视觉、语言与动作统一建模后，任务指令可以自然语言下达，语义与空间信息被映射为具体动作序列。
- 让视觉、语言与动作统一建模。任务指令可以自然语言下达，模型把语义与空间信息映射为具体动作序列。
- 让训练与部署形成循环。真实场景的失败案例回到训练数据，模型能力随实际使用持续提升。本体与算法协同优化，传感器布局、关节自由度与控制策略作为整体设计而非分开选型。
- 让本体与算法协同优化。传感器布局、关节自由度与控制策略被作为整体设计，而非分开选型。
总之，具身机器人可以帮助企业实现自动化从固定工位到开放环境的跨越，让机器能在结构不断变化的真实世界里完成任务，是通用智能在物理侧落地并驱动下一代自动化形态的关键方向。
翻译
Embodied robots are robot systems that act autonomously and interact with the environment in the real world through a closed loop of embodiment, perception and decision making. Its core is completing diverse tasks through learning and adaptation in unstructured environments, with capabilities covering embodiment design, multimodal perception, vision-language-action models and whole-body control. It solves the problem that industrial robots executing preset trajectories cannot handle changing environments, but it does not solve reliability in dexterous manipulation and long-horizon tasks.
At the heart of embodied robots is perception and action forming a closed loop, which can help companies achieve the following:
- Let robots re-decide when the environment changes. After objects shift in position or shape, perception results drive replanning instead of fixed trajectories.
- Unify vision, language and action in one model. Task instructions can be given in natural language, with the model mapping semantics and spatial information into action sequences.
- Close the loop between training and deployment. Real-world failure cases return to training data, so model capability improves with actual use.
- Co-optimize embodiment and algorithms. Sensor placement, joint degrees of freedom and control strategy are designed as one whole rather than selected separately.
In short, embodied robots can help enterprises extend automation from fixed workstations into open environments, let machines complete tasks in constantly changing real-world conditions, and constitute a key direction for bringing general intelligence to the physical side and driving the next generation of automation.
