相关数据集
humanObjectDetection
本数据集用于训练和验证机器学习模型，以区分机器人与三种不同类型物体（人类、铝材和PVC）的接触。数据由Franka Emika Panda机械臂在实时交互中收集，采样率为200Hz。数据集包括85个时间序列用于训练和40个时间序列用于验证。每个时间序列记录了机器人与各自物体之间的多次接触，每个序列添加了3次接触。数据集的创建旨在帮助机器人更好地理解和适应其工作环境，从而提高人机协作的安全性。
arXiv2025-08-04 更新190
Designing Human-AI Collaboration: A Sufficient-Statistic Approach
We propose a sufficient statistic for designing AI information-disclosure and selective automation policies. The approach allows for endogenous and biased beliefs, and effort crowd-out, without using
NBER2025-06-01 更新100
How does the status of errant robot affect our desire for contact? – The moderating effect of team interdependence
Technological breakthroughs such as artificial intelligence and sensors make human-robot collaboration a reality. Robots with highly reliable, specialised skills gain informal status in collaborative
DataCite Commons2024-11-26 更新80
Performance in Human-AI Teams: Experimental Evidence on Commitment Deficits Under Competition
Overview: This research investigates why human-AI teams (HATs) often underperform despite AI's growing capabilities. While previous explanations have centered on coordination, communication failures,
DataCite Commons2025-04-01 更新120
Data underlying the study on the effects of task allocation using human’s willingness in trust and teamwork.
As machines’ autonomy increases, their capacity to learn and adapt to humans in collaborative scenarios increases too. In particular, machines can use artificial trust (AT) to make decisions, such as
DataCite Commons2025-10-03 更新80
