DYAD: A Multimodal Dataset of Co-Located Human Assistance

2026-09-08

AI Summary

为解决共处助手如何有效提供帮助的问题，通过构建同步多模态数据集DYAD，记录并分析人类在齿轮箱组装过程中请求与提供帮助的行为模式。

Abstract

An embodied assistant working beside a person must track task state, recognize help seeking, choose how to intervene, and produce an appropriate response. Existing procedural datasets richly describe individual execution, while interactive datasets capture remote verbal instruction or undifferentiated co-working. They do not jointly link a co-located helper's verbal and physical interventions to performer requests, task state, assistance triggers, and outcomes. We introduce DYAD (DYadic Assistance Dataset), a synchronized multimodal record of human-human assistance during gearbox assembly. Across 20 sessions, one trained helper follows a guidance-first policy while assisting HoloLens 2 wearers. DYAD links 528 task-step intervals and 611 performer requests with 851 valid assistance records spanning verbal and physical help. DYAD's annotations span the assistance process; three reference tasks evaluate selected components rather than an end-to-end system: causal step understanding, pre-onset mode anticipation, and instructor response generation. On 829 eligible mode events, the strongest four-seed RGB mean is 0.548 +/- 0.007 macro-F1; causal metadata reaches 0.624 and a privileged trigger mapping 0.915, revealing information not recovered from pre-onset RGB. DYAD's contribution is not scale, but a linked interaction structure spanning help seeking, intervention choice, execution, and outcome under egocentric and workspace sensing.

Problem

• Co-located Assistance
• Multimodal Dataset
• Human-human Interaction
• Task State
• Help Seeking

Innovation

• Multimodal Dataset
• Co-located Assistance
• Synchronized Record
• Human-Human Interaction
• Egocentric and Workspace Sensing