双臂机械臂操作中时间任务结构与动作时机的统一学习
Unified Learning of Temporal Task Structure and Action Timing for Bimanual Robot Manipulation
AI总结：
本文提出了一种统一学习双臂机械臂时间任务结构与动作时机的方法，通过符号和亚符号时间约束生成可执行的执行计划。
摘要：
时间任务结构是双臂操作的基础：机器人不仅要知道一个动作在另一个动作之前或重叠，还要知道每个动作何时发生以及应持续多长时间。虽然符号时间关系可以实现对任务结构和替代执行序列的高层推理，但具体的定时参数对于协调两个手的执行层面同样至关重要。现有方法将这两个层面孤立处理，导致高层任务规划与低层运动同步之间存在差距。本文提出了一种从人类示范中学习符号和亚符号时间任务约束的方法，并推导出可用于双臂操作的可执行、时间参数化的计划。我们的贡献包括（i）一种三维表示两个动作之间时间的方法，基于多元高斯混合模型来表示动作之间的亚符号时间关系；（ii）一种基于Davis-Putnam-Logemann-Loveland（DPLL）算法的方法，找到并排序所有无矛盾的Allen关系分配给动作对，代表任务的不同模式；（iii）一种基于优化的规划系统，将识别出的符号和亚符号时间任务约束结合起来，推导出可用于机器人执行的时间参数化计划。我们在几个数据集上评估了我们的方法，证明我们的方法生成的时间参数化计划比最显著的示范基线更接近人类示范。
英文摘要：
Bimanual manipulation requires both temporal task structure - which actions precede or overlap others - and concrete timing - when each action starts and how long it takes. Symbolic relations enable high-level reasoning about task structure and alternative execution sequences, while concrete timing parameters are essential for coordinating two hands at the execution level. Existing approaches address these two levels in isolation, leaving a gap between task planning and movement synchronization. This work presents an approach for learning symbolic and subsymbolic temporal task constraints from human demonstrations and deriving executable, temporally parametrized plans for bimanual manipulation. Specifically, our contributions are (i) a 3-D representation of timings 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 algorithm that finds and ranks all contradiction-free Allen relation assignments of a task, representing its modes, and (iii) an optimization-based planning system combining the identified symbolic and subsymbolic temporal task constraints to derive parametrized plans for robot execution. We quantitatively evaluate our approach and show that the inferred symbolic task constraints are more accurate than those of prior heuristic approaches and that the generated plans are temporally closer to human demonstrations than the most characteristic demonstration. Additionally, we qualitatively demonstrate its applicability through orchestrated task executions in simulation and on real robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
