Silicon Language: A Robot-Native Knowledge Exchange Framework for Heterogeneous Robots
作者: Yi Liu, Xianglin Meng, Chang Chen, Jingjing Fan
分类: cs.RO
发布日期: 2026-10-06
备注: 13 pages, 10 figures
💡 一句话要点
提出Silicon Language框架以解决异构机器人知识共享问题
🎯 匹配领域: 支柱七：动作重定向 (Motion Retargeting)
关键词: 异构机器人 知识交换 能力转移 自主学习 机器人框架 实验验证 能力适配
📋 核心要点
- 现有方法在异构机器人之间重用能力时，需大量人力进行适配和验证，效率低下。
- Silicon Language框架允许机器人主动编码经验、发布知识包并进行自主评估，从而简化能力转移过程。
- 实验结果显示，能力适配时间从几天缩短至几小时，初步证明了跨实体知识交换的可行性。
📝 摘要（中文）
在异构机器人之间重用能力仍需大量人力适配和验证，技能转移往往意味着重新设计接口、调整参数和重新验证安全性。本文提出Silicon Language，一个机器人原生的知识交换框架，使机器人能够主动参与自身能力的演变。该框架允许机器人将经验编码为知识包，发布和检索同伴包，并通过独立的本地试验进行评估。接收方的可用性评估程序使每个机器人能够自主判断外部包的实用性，并通过渐进融合与自动回滚设计降低负迁移风险。我们在模拟矿井实验室进行了为期30天的概念验证部署，记录了异构机器人之间的能力编码和翻译过程。
🔬 方法详解
问题定义：本文旨在解决异构机器人之间能力转移的复杂性和低效率问题。现有方法需要大量人力进行接口适配、参数调整和安全验证，导致适配时间长且不灵活。
核心思路：Silicon Language框架的核心思想是将机器人视为其能力演变的主动主体，通过编码经验和知识包的发布与检索，实现自主能力的演进和适配。
技术框架：该框架由三个基础设施层（边缘代理、Silicon Transfer Protocol (STP)和知识中心）以及一个受人类学术系统启发的能力堆栈组成。机器人通过这些模块进行知识的编码、翻译和评估。
关键创新：最重要的创新在于机器人能够自主生成和评估知识包，减少了对人工干预的依赖，并通过渐进融合与自动回滚机制降低了负迁移风险。
关键设计：在设计中，机器人通过独立的本地试验评估知识包的有效性，并采用渐进融合策略来逐步适应新能力，确保系统的安全性和稳定性。
🖼️ 关键图片
📊 实验亮点
实验结果表明，能力适配时间从几天缩短至几小时，显示出Silicon Language框架在异构机器人知识交换中的有效性。通过操作员辅助的文件复制，两个异构机器人成功编码和翻译了三种能力，初步验证了跨实体知识共享的可行性。
🎯 应用场景
该研究的潜在应用领域包括工业自动化、服务机器人和救援机器人等，能够显著提升异构机器人群体的协作能力和适应性。未来，Silicon Language框架可能推动机器人在复杂环境中的自主学习和能力演化，具有重要的实际价值和影响。
📄 摘要（原文）
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.