论文标题
学习和混合机器人在时空上拥抱行为
Learning and Blending Robot Hugging Behaviors in Time and Space
论文作者
论文摘要
我们介绍了一种基于模仿学习的物理人 - 机器人相互作用算法，能够预测涉及多个相互作用叠加的复杂相互作用中适当的机器人响应。我们提出的算法，混合贝叶斯互动原语（B-BIP）使我们能够在复杂的拥抱场景中实现响应式互动，能够往复运动和适应拥抱运动和时机。我们表明，该算法是先前工作的概括，为此，原始配方将其减少到单个相互作用的特定情况，并通过广泛的用户研究和经验实验来评估我们的方法。与现有的最新方法相比，我们的算法在准确性，响应能力和时机方面产生的定量预测误差和更有利的参与者反应。
We introduce an imitation learning-based physical human-robot interaction algorithm capable of predicting appropriate robot responses in complex interactions involving a superposition of multiple interactions. Our proposed algorithm, Blending Bayesian Interaction Primitives (B-BIP) allows us to achieve responsive interactions in complex hugging scenarios, capable of reciprocating and adapting to a hugs motion and timing. We show that this algorithm is a generalization of prior work, for which the original formulation reduces to the particular case of a single interaction, and evaluate our method through both an extensive user study and empirical experiments. Our algorithm yields significantly better quantitative prediction error and more-favorable participant responses with respect to accuracy, responsiveness, and timing, when compared to existing state-of-the-art methods.
