Modeling multimodal human behavior accurately has been a key barrier to increasing the level of interaction between human and robot, particularly for collaborative tasks. Our key insight is that the predictive accuracy of human behaviors on physical tasks is bottlenecked by the model for methods involving human behavior prediction. We present a method for training denoising diffusion probabilistic models on a dataset of collaborative human-human demonstrations and conditioning on past human partner actions to plan sequences of robot actions that synergize well with humans during test time. We demonstrate the method outperforms other state-of-art learning methods on human-robot table-carrying, a continuous state-action task, in both simulation and real settings with a human in the loop. Moreover, we qualitatively highlight compelling robot behaviors that arise during evaluations that demonstrate evidence of true human-robot collaboration, including mutual adaptation, shared task understanding, leadership switching, learned partner behaviors, and low levels of wasteful interaction forces arising from dissent. Project page coming soon.

翻译：精确建模人类多模态行为一直是提升人机交互水平的关键障碍，尤其在协作任务中尤为突出。本文的核心洞见在于：涉及人类行为预测的方法中，物理任务中人类行为的预测准确性受限于模型本身。我们提出一种基于协作型人人演示数据集训练去噪扩散概率模型的方法，并通过基于人类合作伙伴的历史动作进行条件约束，在测试阶段规划出能与人类高效协同的机器人动作序列。我们以连续状态动作空间的人机协作搬桌子任务为例，在仿真和真实人类参与的闭环实验中验证了该方法优于现有最先进的机器学习方法。此外，我们通过定性分析凸显了评估中涌现的极具说服力的机器人行为，这些行为证明了真正人机协作的存在——包括相互适应、共享任务理解、主导权切换、学习合作伙伴行为模式，以及因分歧产生的低效交互力。项目页面即将上线。