硅语言：面向异构机器人的机器人原生知识交换框架
Silicon Language: A Robot-Native Knowledge Exchange Framework for Heterogeneous Robots
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中文总结 AI 辅助
提出硅语言框架，让异构机器人通过知识包编码、发布、检索、转换和本地试验实现能力复用，部署试验表明适配时间从数天降至数小时。
中文摘要 AI 辅助
在异构机器人之间复用一项能力仍然需要大量的人工适配和验证：迁移一项技能通常意味着重新设计接口、重新调整参数以及重新验证安全性。我们提出了硅语言（Silicon Language），一个机器人原生的知识交换框架，将机器人视为其自身能力演化的主动主体。在该框架中，想要获得某项能力的机器人会将其自身经验编码为知识包，发布这些知识包，检索同伴的知识包，将其转换以适应自身的传感器和执行器，并通过独立的本地试验进行审查。接收方的可用性评估流程使每个机器人能够自行判断外部知识包是否有用，而渐进式混合与自动回滚机制旨在降低采纳外部知识包时负迁移的风险。该系统将三个基础设施层（边缘代理、硅传输协议（STP）和知识中心）与一个受人类学术体系启发的能力栈相结合。我们报告了在榆林一个地上模拟矿山实验室进行的为期30天的概念验证部署，随后在室外沙土道路和室内工厂车间进行了试验。两个异构机器人通过操作员辅助的文件复制，经由硅语言转换层，编码并转换了三种跨实施例的能力；尘封跟随行为的源端试验在金牛座（Taurus）上进行了记录。项目记录表明，每项能力的适配时间从数天缩短至数小时；我们将这些数据作为描述性部署记录而非受控测量结果呈现。该部署为跨实施例知识交换提供了初步证据；车队级自主演化以及有/无知识包的受控对比研究仍是未来工作。
英文摘要
Reusing a capability across heterogeneous robots still requires substantial human adaptation and verification: transferring a skill often means re-engineering interfaces, retuning parameters, and re-validating safety. We introduce Silicon Language, a robot-native knowledge exchange framework that treats the robot as the active subject of its own capability evolution. In this framework, a robot that wants a capability encodes its own experience into knowledge packets, publishes them, retrieves peer packets, translates them for its own sensors and actuators, and reviews them through independent local trial. A receiver-side usability evaluation procedure lets each robot decide for itself whether an external packet is useful, and progressive blending with automatic rollback is designed to reduce the risk of negative transfer when adopting it. The system combines three infrastructure layers (edge agent, Silicon Transfer Protocol (STP), and knowledge hub) with a capability stack inspired by the human scholarly system. We report a 30-day proof-of-concept deployment at an above-ground simulated-mine laboratory in Yulin, with following trials on an outdoor sand road and an indoor factory floor. Two heterogeneous robots encoded and translated three capabilities across embodiments through operator-assisted file copies mediated by the Silicon Language translation layer; source-side trials of the dust-locked following behavior were recorded on Taurus. Project records indicate that per-capability adaptation time dropped from days to hours; we present these figures as descriptive deployment records rather than controlled measurements. The deployment provides initial evidence for cross-embodiment knowledge exchange; fleet-level autonomous evolution and controlled with/without-packet comparisons remain future work.
发表机构
- Beijing Institute of Technology（北京理工大学）
- Yulin Saiyi Intelligent Technology Co., Ltd.（榆林赛亿智能科技有限公司）
- Yulin Intelligent Unmanned Equipment Innovation Center Co., Ltd.（榆林智能无人装备创新中心有限公司）
机构由 AI 辅助整理，请以论文原文为准。
