Robots can use auditory, visual, or haptic interfaces to convey information to human users. The way these interfaces select signals is typically pre-defined by the designer: for instance, a haptic wristband might vibrate when the robot is moving and squeeze when the robot stops. But different people interpret the same signals in different ways, so that what makes sense to one person might be confusing or unintuitive to another. In this paper we introduce a unified algorithmic formalism for learning co-adaptive interfaces from scratch. Our method does not need to know the human's task (i.e., what the human is using these signals for). Instead, our insight is that interpretable interfaces should select signals that maximize correlation between the human's actions and the information the interface is trying to convey. Applying this insight we develop LIMIT: Learning Interfaces to Maximize Information Transfer. LIMIT optimizes a tractable, real-time proxy of information gain in continuous spaces. The first time a person works with our system the signals may appear random; but over repeated interactions the interface learns a one-to-one mapping between displayed signals and human responses. Our resulting approach is both personalized to the current user and not tied to any specific interface modality. We compare LIMIT to state-of-the-art baselines across controlled simulations, an online survey, and an in-person user study with auditory, visual, and haptic interfaces. Overall, our results suggest that LIMIT learns interfaces that enable users to complete the task more quickly and efficiently, and users subjectively prefer LIMIT to the alternatives. See videos here: https://youtu.be/IvQ3TM1_2fA.

翻译：机器人可通过听觉、视觉或触觉接口向人类用户传递信息。这些接口选择信号的方式通常由设计者预先定义：例如，当机器人移动时触觉腕带可能振动，停止时则收紧。但不同的人对相同信号的解读方式各异，因此对某人有意义的信号可能令他人困惑或违反直觉。本文提出一种统一的算法形式化框架，用于从零开始学习协同自适应接口。我们的方法无需预知人类任务（即人类使用这些信号的目的）。相反，我们的洞见在于：可解释的接口应选择能够最大化人类行为与接口试图传递信息之间相关性的信号。基于这一洞见，我们开发了LIMIT（Learning Interfaces to Maximize Information Transfer）。LIMIT在连续空间中优化了一个可计算的、实时的信息增益代理指标。当用户首次与我们的系统交互时，信号可能看似随机；但通过重复交互，接口会学习显示信号与人类响应之间的一一映射。最终方法既具备当前用户个性化特征，又不局限于任何特定的接口模态。我们通过受控仿真、在线调查以及包含听觉、视觉和触觉接口的实地用户研究，将LIMIT与最先进的基线方法进行了比较。总体而言，结果表明LIMIT学习的接口能让用户更快速、高效地完成任务，并且用户主观上更偏好LIMIT而非替代方案。视频见：https://youtu.be/IvQ3TM1_2fA。