Quadruped robots are showing impressive abilities to navigate the real world. If they are to become more integrated into society, social trust in interactions with humans will become increasingly important. Additionally, robots will need to be adaptable to different humans based on individual preferences. In this work, we study the social interaction task of learning optimal handshakes for quadruped robots based on user preferences. While maintaining balance on three legs, we parameterize handshakes with a Central Pattern Generator consisting of an amplitude, frequency, stiffness, and duration. Through 10 binary choices between handshakes, we learn a belief model to fit individual preferences for 25 different subjects. Our results show that this is an effective strategy, with 76% of users feeling happy with their identified optimal handshake parameters, and 20% feeling neutral. Moreover, compared with random and test handshakes, the optimized handshakes have significantly decreased errors in amplitude and frequency, lower Dynamic Time Warping scores, and improved energy efficiency, all of which indicate robot synchronization to the user's preferences. Video results can be found at https://youtu.be/elvPv8mq1KM .

翻译：四足机器人在现实世界导航方面展现出令人印象深刻的能力。若要让它们更好地融入人类社会，建立人机交互中的社会信任将变得日益重要。此外，机器人需要根据个体偏好适应不同人类用户。本研究针对四足机器人学习基于用户偏好的最优握手策略这一社交交互任务展开探索。在保持三足平衡的前提下，我们采用由振幅、频率、刚度和持续时间参数构成的中枢模式发生器对握手动作进行参数化建模。通过对25名不同受试者进行10组握手动作的二元选择实验，我们建立了能够拟合个体偏好的置信度模型。实验结果表明该策略具有显著效果：76%的用户对识别出的最优握手参数表示满意，20%持中立态度。与随机握手及测试握手相比，优化后的握手动作在振幅和频率误差方面显著降低，动态时间规整得分更优，能量效率得到提升，这些指标均表明机器人能够有效适应用户偏好。视频结果可访问 https://youtu.be/elvPv8mq1KM 查看。