Robotics in Logistics: The Rise of Non-Humanoid Sorting Robots
机器人进厂第一课：不当人
The article explores the deployment of non-humanoid robots in logistics, focusing on parcel sorting. It discusses the technical and economic advantages of these systems, including their ability to handle high-frequency, non-structured tasks, and the shift from demonstration to real-world deployment. Key players like StarDance, SelfVariable, and Jiajia Vision are highlighted, along with their approaches to automation in logistics scenarios.
The article highlights the growing adoption of non-humanoid robots in logistics, particularly in parcel sorting, emphasizing the practicality of these systems over human-like forms. It outlines the technical and economic rationale for deploying such robots, including their ability to handle high-frequency, non-structured tasks with minimal human intervention.
Source: Chinese robotics — Company news discovery · Read original article ↗
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一年前， 机器人 还在展台上叠衣服、煎荷包蛋、做汉堡。
Open source S10
一年后，已经有一批机器人进入物流行业干起了分拣，有趣的是，新上岗的机器人分拣员，没有一个是完整的人形。
Open source S11
它相当于自动分拣线的“喂料口”，而“喂料口”这个岗位，恰好是具身智能最缺的那种数据工厂。
Open source S16
“供包是典型的非结构化作业，包裹堆叠、品类和姿态随机，几乎没有两个完全一样的现场，靠固定编程和传统工装做不了，必须依赖具身大脑的智能泛化能力；同时它要求精细的手眼协同（抓取、翻面、轻放），我们希望在具身智能进化的过程中能够在产业中先用起来，先成为生产力。
Open source S20
现场每天产生的真实数据能持续回流，也让机器人越干越熟练，也是让具身智能进化不可或缺的燃料。
Open source S21
分拣恰好是回流制：夹错了、放反了，件会回流重来，回流率3%对人工2%，差的那1个点是成本，不是事故。
Open source S24
工厂装配动作太单一，数据多样性差；家庭环境太开放，反馈不明确，频次也低。
Open source S19
一个理想的数据工厂要同时满足三个条件： 作业频次高、明确成败反馈、场景能复制。
Open source S18
Source：Chinese robotics — Company news discovery · finance.sina.com.cn