# IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants Source: [IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants]() Collection index: [llms.txt]() Data updated: 2025-11-27T05:04:35.166Z Authors: Vivek Chavan, Yasmina Imgrund, Tung Dao, Sanwantri Bai, Bosong Wang, Ze Lu, Oliver Heimann, J\\"org Kr\\"uger Published: 2025-11-24T20:45:17.000Z ## Abstract We introduce IndEgo, a multimodal egocentric and exocentric dataset addressing common industrial tasks, including assembly/disassembly, logistics and organisation, inspection and repair, woodworking, and others. The dataset contains 3,460 egocentric recordings (approximately 197 hours), along with 1,092 exocentric recordings (approximately 97 hours). A key focus of the dataset is collaborative work, where two workers jointly perform cognitively and physically intensive tasks. The egocentric recordings include rich multimodal data and added context via eye gaze, narration, sound, motion, and others. We provide detailed annotations (actions, summaries, mistake annotations, narrations), metadata, processed outputs (eye gaze, hand pose, semi-dense point cloud), and benchmarks on procedural and non-procedural task understanding, Mistake Detection, and reasoning-based Question Answering. Baseline evaluations for Mistake Detection, Question Answering and collaborative task understanding show that the dataset presents a challenge for the state-of-the-art multimodal models. Our dataset is available at: https://huggingface.co/datasets/FraunhoferIPK/IndEgo ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()