Temporal task structure is fundamental for bimanual manipulation: a robot must not only know that one action precedes or overlaps another, but also when each action should occur and how long it should take. While symbolic temporal relations enable high-level reasoning about task structure and alternative execution sequences, concrete timing parameters are equally essential for coordinating two hands at the execution level. Existing approaches address these two levels in isolation, leaving a gap between high-level task planning and low-level movement synchronization. This work presents an approach for learning both symbolic and subsymbolic temporal task constraints from human demonstrations and deriving executable, temporally parametrized plans for bimanual manipulation. Our contributions are (i) a 3-dimensional representation of timings between two actions with methods based on multivariate Gaussian Mixture Models to represent temporal relationships between actions on a subsymbolic level, (ii) a method based on the Davis-Putnam-Logemann-Loveland (DPLL) algorithm that finds and ranks all contradiction-free assignments of Allen relations to action pairs, representing different modes of a task, and (iii) an optimization-based planning system that combines the identified symbolic and subsymbolic temporal task constraints to derive temporally parametrized plans for robot execution. We evaluate our approach on several datasets, demonstrating that our method generates temporally parametrized plans closer to human demonstrations than the most characteristic demonstration baseline.

翻译：时间任务结构是双手机器人操作的基础：机器人不仅需要知道一个动作先于或重叠于另一个动作，还需要知道每个动作应当何时发生以及持续多长时间。虽然符号化时间关系支持对任务结构和替代执行序列的高层推理，但具体的时间参数对于执行层面协调双手动作同样至关重要。现有方法分别处理这两个层面，导致高层任务规划与低层运动同步之间存在鸿沟。本研究提出一种从人类演示中同时学习符号化与亚符号化时间任务约束，并推导出可执行的、时间参数化双手机器人操作规划的方法。我们的贡献包括：（i）基于多元高斯混合模型的三维动作间时序表示方法，用于在亚符号层面表征动作间的时间关系；（ii）基于Davis-Putnam-Logemann-Loveland（DPLL）算法的方法，用于发现并排序所有无矛盾的Allen关系动作对分配方案，表征任务的不同执行模式；（iii）基于优化的规划系统，结合已识别的符号化与亚符号化时间任务约束，推导出机器人执行所需的时间参数化规划。我们在多个数据集上评估了所提方法，实验表明相较于最具代表性的演示基线方法，我们的方法能够生成更接近人类演示的时间参数化规划。