用用户功能评估数据预测个性化的动作能力，让机器人更贴心地辅助照护。
GRACE: Generalizing Robot-Assisted Caregiving with User Functionality Embeddings
- 通过职业治疗评分生成用户身体功能嵌入向量，实现个性化动作能力预测。
- 无需运动捕捉即可为新用户预测功能活动范围，准确率显著提升。
- 适用于需要个性化支持的居家照护场景，尤其适合行动受限人群。
机器人照护需个性化以满足照护对象的多样化需求——在协助完成手递物、沐浴、穿衣及康复等任务时，应兼顾用户的自主行动能力。其中关键差异在于个体间的功能活动范围（fROM），其差异显著。本文提出一种新型数据驱动方法，基于职业治疗中的功能评估分数，预测个性化fROM，以增强机器人在多种照护任务中的泛化决策能力。我们构建了一个神经模型，将功能评估分数映射为用户身体功能的潜在表示，并使用模拟移动障碍用户的运动捕捉数据进行训练。训练后，该模型可在无运动捕捉条件下，为新用户预测个性化fROM。通过仿真实验与真实机器人用户研究验证，本模型所生成的个性化预测可使机器人提供更精准有效的辅助，同时提升用户在行动中的自主性。更多信息请访问：https://emprise.cs.cornell.edu/grace/
原文摘要 · Abstract (English)
Robot caregiving should be personalized to meet the diverse needs of care recipients -- assisting with tasks as needed, while taking user agency in action into account. In physical tasks such as handover, bathing, dressing, and rehabilitation, a key aspect of this diversity is the functional range of motion (fROM), which can vary significantly between individuals. In this work, we learn to predict personalized fROM as a way to generalize robot decision-making in a wide range of caregiving tasks. We propose a novel data-driven method for predicting personalized fROM using functional assessment scores from occupational therapy. We develop a neural model that learns to embed functional assessment scores into a latent representation of the user's physical function. The model is trained using motion capture data collected from users with emulated mobility limitations. After training, the model predicts personalized fROM for new users without motion capture. Through simulated experiments and a real-robot user study, we show that the personalized fROM predictions from our model enable the robot to provide personalized and effective assistance while improving the user's agency in action. See our website for more visualizations: https://emprise.cs.cornell.edu/grace/.
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