Developing robots that can assist humans efficiently, safely, and adaptively is crucial for real-world applications such as healthcare. While previous work often assumes a centralized system for co-optimizing human-robot interactions, we argue that real-world scenarios are much more complicated, as humans have individual preferences regarding how tasks are performed. Robots typically lack direct access to these implicit preferences. However, to provide effective assistance, robots must still be able to recognize and adapt to the individual needs and preferences of different users. To address these challenges, we propose a novel framework in which robots infer human intentions and reason about human utilities through interaction. Our approach features two critical modules: the anticipation module is a motion predictor that captures the spatial-temporal relationship between the robot agent and user agent, which contributes to predicting human behavior; the utility module infers the underlying human utility functions through progressive task demonstration sampling. Extensive experiments across various robot types and assistive tasks demonstrate that the proposed framework not only enhances task success and efficiency but also significantly improves user satisfaction, paving the way for more personalized and adaptive assistive robotic systems. Code and demos are available at https://asonin.github.io/Human-Aware-Assistance/.

翻译：开发能够高效、安全且自适应地辅助人类的机器人对于医疗保健等现实世界应用至关重要。尽管先前的研究通常假设存在一个集中式系统来协同优化人机交互，但我们认为现实场景要复杂得多，因为人类对于任务执行方式存在个体偏好。机器人通常无法直接获取这些隐含偏好。然而，为了提供有效辅助，机器人必须能够识别并适应不同用户的个体需求与偏好。为应对这些挑战，我们提出了一种新颖框架，使机器人能够通过交互推断人类意图并推理其效用函数。我们的方法包含两个关键模块：预测模块作为一个运动预测器，捕捉机器人代理与用户代理之间的时空关系，从而有助于预测人类行为；效用模块则通过渐进式任务演示采样来推断潜在的人类效用函数。在不同机器人类型和辅助任务上进行的大量实验表明，所提出的框架不仅能提升任务成功率与效率，还能显著提高用户满意度，为更个性化、自适应的辅助机器人系统开辟了道路。代码与演示可在 https://asonin.github.io/Human-Aware-Assistance/ 获取。