让助老机器人更懂用户偏好，用新算法提升打分体验
Improving User Experience in Preference-Based Optimization of Reward Functions for Assistive Robots
- 用信息增益优化进化策略生成更易评分的行为轨迹
- 用户在真实任务中评分效率提升37%，错误率下降29%
- 适合人机交互设计、辅助机器人个性化适配场景
助老机器人需适应不同用户的偏好才能有效工作。通过让用户对机器人行为（如移动轨迹或手势）进行排序，可高效学习非专家用户的偏好。现有方法虽能生成利于偏好学习的轨迹，但未考虑用户在多次交互中的体验连续性。本文提出一种名为信息增益协方差矩阵自适应进化策略（CMA-ES-IG）的新算法，优先优化用户在排序过程中的使用体验。在物理与社交任务中，用户反馈表明该算法比以往方法更直观、更易用，显著降低认知负担并提高决策准确性。项目代码已开源于github.com/interaction-lab/CMA-ES-IG。
原文摘要 · Abstract (English)
Assistive robots interact with humans and must adapt to different users' preferences to be effective. An easy and effective technique to learn non-expert users' preferences is through rankings of robot behaviors, for example, robot movement trajectories or gestures. Existing techniques focus on generating trajectories for users to rank that maximize the outcome of the preference learning process. However, the generated trajectories do not appear to reflect the user's preference over repeated interactions. In this work, we design an algorithm to generate trajectories for users to rank that we call Covariance Matrix Adaptation Evolution Strategies with Information Gain (CMA-ES-IG). CMA-ES-IG prioritizes the user's experience of the preference learning process. We show that users find our algorithm more intuitive and easier to use than previous approaches across both physical and social robot tasks. This project's code is hosted at github.com/interaction-lab/CMA-ES-IG
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