工业灵巧性基准测试：一个用于工业灵巧操作的硬件-软件基准测试平台

中文摘要 AI 辅助

灵巧操作仍是工业自动化的关键瓶颈，如电缆布线等任务仍依赖人工。本文从经典模块化机器人流程向工业灵巧操作的端到端多模态模仿学习框架发展。贡献包括：一套工业灵巧性基准测试（IDB）板；可扩展模仿学习框架（DAG-ROS）；多模态扩散策略框架（AG-iDP3）。以数据中心电缆操作为例评估，最佳配置多模态扩展扩散策略（DP）的抓取和插入组合任务成功率达78%，远超单相机RGB DP基线的36%，且每个任务阶段仅需约100次遥控演示。结果表明正确学习的策略在多方面优于传统方法，有利于向可扩展机器人自动化转变。

英文摘要

Dexterous manipulation remains a critical bottleneck in industrial automation; tasks such as cable routing, connector insertion, and precision assembly still rely heavily on manual labor despite decades of robotics research. This work presents a progression from classical, modular robotics pipelines toward an end-to-end multimodal imitation-learning framework for industrial dexterous manipulation. As a part of this work, we introduce three key contributions: a set of Industrial Dexterity Benchmark (IDB) boards aimed to mimic datacenter cable management, automotive cable harnesses, and gearbox assembly tasks; a scalable imitation learning framework (DAG-ROS); and a multimodal diffusion-based policy framework (AG-iDP3) that creates models fusing RGB images, point clouds, joint positions, and wrist-frame wrench data. Focusing on the datacenter cable manipulation board, we evaluate the performance of a task involving cleaning a single cable over variations of an end-to-end AI policy using 48 trials per configuration. The best performing configuration, a multimodal expansion Diffusion Policy (DP), includes a multi-view RGB image source passed through an R3M encoder and reaches a 78% grasp and insert combined task success rate. This performance marks a significant improvement over the 36% observed from the single-camera RGB DP baseline. Each of the tested configurations requires only approximately 100 teleoperated demonstrations per task phase. These results indicate that the correct learned policy can outperform classical vision and control robotic methods in robustness, generalization, and deployment efficiency, justifying a shift toward scalable robotic automation for high up-time industrial environments.

发表机构

• Analog Devices, Inc.（亚德诺半导体技术有限公司）

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