论文标题
通过时空对象建模的主动机器人援助
Proactive Robot Assistance via Spatio-Temporal Object Modeling
论文作者
论文摘要
主动的机器人援助使机器人能够预测并满足用户的需求，而无需明确询问。我们将主动的帮助作为机器人的问题，预测与日常用户例程相关的对象运动的时间模式，并通过放置对象来适应环境满足其需求，从而主动协助用户。我们引入了一个生成图神经网络，以从对象布置的时间序列中学习对象动力学的统一时空预测模型。我们还从日常工作（Homer）数据集中贡献了家庭对象运动，该数据集跟踪与五个模拟家庭的50天以上日常生活相关的家庭对象。我们的模型在预测对象移动方面的表现优于主要基准，正确预测了11.1％的对象的位置，并错误地预测了人类用户使用的对象的11.5％的位置。
Proactive robot assistance enables a robot to anticipate and provide for a user's needs without being explicitly asked. We formulate proactive assistance as the problem of the robot anticipating temporal patterns of object movements associated with everyday user routines, and proactively assisting the user by placing objects to adapt the environment to their needs. We introduce a generative graph neural network to learn a unified spatio-temporal predictive model of object dynamics from temporal sequences of object arrangements. We additionally contribute the Household Object Movements from Everyday Routines (HOMER) dataset, which tracks household objects associated with human activities of daily living across 50+ days for five simulated households. Our model outperforms the leading baseline in predicting object movement, correctly predicting locations for 11.1% more objects and wrongly predicting locations for 11.5% fewer objects used by the human user.
