论文标题
镜子：可辅助人机交流的可区分的深层社会预测
MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication
论文作者
论文摘要
交流是情报的标志。在这项工作中，我们介绍了镜像，一种方法（i）从人类示范中快速学习人类模型，（ii）在辅助共享控制环境中使用这些模型来进行后续的沟通计划。镜像的灵感来自社会投影理论，该理论假设人类使用自模型来理解他人。同样，镜像利用了使用强化学习来引导人类建模的自我模型。与现有的行为克隆和最新的模仿学习方法相比，使用模拟人类的实验表明，这种方法会导致快速学习和更健壮的模型。我们还使用Carla模拟器提出了一项人类受试者的研究，该研究表明，（i）镜像能够扩展到具有高维观测值和复杂世界物理学的复杂域，并且（ii）提供有效的辅助沟通，使参与者能够在不利天气条件下更安全地推动。
Communication is a hallmark of intelligence. In this work, we present MIRROR, an approach to (i) quickly learn human models from human demonstrations, and (ii) use the models for subsequent communication planning in assistive shared-control settings. MIRROR is inspired by social projection theory, which hypothesizes that humans use self-models to understand others. Likewise, MIRROR leverages self-models learned using reinforcement learning to bootstrap human modeling. Experiments with simulated humans show that this approach leads to rapid learning and more robust models compared to existing behavioral cloning and state-of-the-art imitation learning methods. We also present a human-subject study using the CARLA simulator which shows that (i) MIRROR is able to scale to complex domains with high-dimensional observations and complicated world physics and (ii) provides effective assistive communication that enabled participants to drive more safely in adverse weather conditions.
