GOLEM：面向电动汽车电池拆解的模块化人形自主系统
GOLEM: Modular Humanoid Autonomy Towards Electric Vehicle Battery Disassembly
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中文总结 AI 辅助
该研究针对人工拆解报废EV电池的痛点，提出适配Unitree H1-2人形机器人的GOLEM开源模块化架构，通过仿真与现实实验验证了其在电池拆解各模块的性能，可用于人形机器人相关能力的公平对比与开发。
中文摘要 AI 辅助
报废电动汽车（EV）电池组的拆解工作枯燥且危险，目前几乎完全由人工完成。本文提出GOLEM（Generalized Open Library of Embodied Modules，通用化具身模块开放库），这是一套用于电动汽车电池拆解的端到端开源系统架构，适配Unitree H1-2人形机器人；其中行走、操作、动态稳定性、导航和空间记忆均为独立模块，具备抽象接口，便于方法的开发、替换与对比。GOLEM以基于Docker的ROS 2抽象层部署，其中MuJoCo和IsaacLab数字孪生体提供与物理机器人匹配的接口。GOLEM的可组合性及单模块定制能力支持从仿真到现实的人形机器人电动汽车电池拆解开发与演示。GOLEM为人形机器人模块提供公平对比，可按能力阶梯评估：一次仅对一个模块进行表征并作为阶梯添加；LiDAR-惯性导航可使机器人定位在6米目标的13.0厘米范围内；学习型站立控制器可从基于采样的下半身MPC无法处理的外部干扰中恢复；对真实现代Ioniq 5电池组上松动紧固件的抓取成功率，在系泊状态下为97%，自由站立状态下降至87%，在导航引起的位姿偏差下降至37%。源代码可在项目页面获取，链接为[this https URL]
英文摘要
Disassembling end-of-life electric vehicle (EV) battery packs is dull and dangerous work, performed almost entirely by humans. We present GOLEM (Generalized Open Library of Embodied Modules), an end-to-end, open-source system architecture for EV battery disassembly with the Unitree H1-2 humanoid robot in which walking, manipulation, dynamic stability, navigation, and spatial memory are independent modules with abstract interfaces, so that methods are easily developed, interchanged, and compared. GOLEM is deployed as a Docker-based ROS 2 abstraction in which MuJoCo and IsaacLab digital twins expose interfaces matching the physical robot. GOLEM's composability and per-module customization enable development and demonstration of humanoid EV battery disassembly, from simulation to reality. GOLEM provides fair comparison between humanoid modules, enabling evaluation as a capability ladder, in which one module is characterized at a time and added as a rung: LiDAR-inertial navigation places the robot within 13.0cm of a 6m goal; a learned standing controller recovers from external disturbances that sampling-based lower-body MPC does not; and grasping loosened fasteners from a real Hyundai Ioniq 5 pack degrades from 97% tethered to 87% free-standing to 37% under navigation-induced pose variance. Source code is available at the project page https://golem-humanoid.github.io
发表机构
- University of Colorado Boulder（科罗拉多大学博尔德分校）
- University of Notre Dame（圣母大学）
机构由 AI 辅助整理，请以论文原文为准。
