Industrial Dexterity Benchmark: A Hardware-Software Benchmarking Platform for Industrial Dexterous Manipulation

2026-07-15

AI Summary

Industrial dexterous manipulation tasks—such as cable routing and connector insertion—remain heavily reliant on human labor due to their automation challenges. This work proposes an end-to-end multimodal imitation learning framework featuring three key contributions: the establishment of a benchmark platform for industrial dexterous manipulation (IDB), the design of a scalable DAG-ROS-based imitation learning architecture, and the development of a diffusion policy, AG-iDP3, which fuses multi-view RGB images, point clouds, joint states, and wrist torque measurements. Leveraging the R3M encoder, the system enables end-to-end training and achieves a combined success rate of 78% in grasping and inserting cables in a data center setting using only approximately 100 human demonstrations—substantially outperforming a single-camera baseline that attains just 36% success.

Abstract

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.

Problem

• dexterous manipulation
• industrial automation
• cable routing
• connector insertion
• precision assembly

Innovation

• dexterous manipulation
• multimodal imitation learning
• diffusion policy
• industrial robotics
• benchmarking platform