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

本研究解决了现有辅助机器人交互中用户偏好学习不够有效的问题。提出了一种名为CMA-ES-IG的算法，通过优先考虑用户体验，生成用户排名的轨迹。实验证明，该算法相比于传统方法在身体和社交机器人任务中更为直观易用，具有显著提高用户体验的潜力。

基于偏好的奖赏函数优化中的用户体验提升