AI 中文总结
本研究提出基于OpenArm的实验室移动操作原型，通过表示交接整合多模块，可暴露部署阻碍并为具身系统整合提供调试接口。
AI 中文摘要
开源机器人技术与基础模型降低了具身人工智能的门槛，但语言引导的实验室自动化仍需要将指令和观测可靠地对齐至安全动作。本领域报告提出了一种基于OpenArm的实验室任务移动操作原型，该原型集成了双OpenArm机械臂、移动基座、垂直滑台、RGB-D传感、基于激光雷达的建图、ROS2/MoveIt执行框架以及轮廓定义的技能接口。系统围绕表示交接构建：将自然语言请求约束为已注册的技能调用，将传感器观测映射至地图与物体位姿，利用物体先验提供角色与技能约束，运行时绑定将验证后的技能编译为可执行的运动目标。我们通过试运行轨迹与启动检查评估该集成路径，结果显示该原型能将缺失校准、不完整物体资产、未完成的真实场景视觉定位等问题暴露为明确的部署阻碍。这些中间表示为在具身系统中整合语言、感知、规划与机器人安全提供了实用的调试接口。
英文摘要
Open-source robotics and foundation models have lowered the barrier to embodied AI, yet language-guided laboratory automation still requires reliable alignment from instructions and observations to safe actions. This field report presents an OpenArm-based mobile manipulation prototype for laboratory-style tasks, built by integrating dual OpenArm manipulators with a mobile base, vertical slide, RGB-D sensing, lidar-based mapping, ROS2/MoveIt execution, and profile-defined skill interfaces. The system is organized around representation handoffs: natural language requests are constrained into registered skill calls, sensor observations are grounded into maps and object poses, object priors provide role and skill constraints, and runtime bindings compile validated skills into executable motion goals. We use dry-run traces and startup checks to evaluate this integration path, showing how the prototype exposes missing calibration, incomplete object assets, and unfinished real-scene visual grounding as explicit deployment blockers. These intermediate representations serve as practical debugging interfaces for integrating language, perception, planning, and robot safety in embodied systems.
CommentsRobotics: Science and Systems (RSS) Workshop 2026
