Bimanual and humanoid robots are appealing because of their human-like ability to leverage multiple arms to efficiently complete tasks. However, controlling multiple arms at once is computationally challenging due to the growth in the hybrid discrete-continuous action space. Task and Motion Planning (TAMP) algorithms can efficiently plan in hybrid spaces but generally produce plans, where only one arm is moving at a time, rather than schedules that allow for parallel arm motion. In order to extend TAMP to produce schedules, we present ScheduleStream, the first general-purpose framework for planning & scheduling with sampling operations. ScheduleStream models temporal dynamics using hybrid durative actions, which can be started asynchronously and persist for a duration that's a function of their parameters. We propose domain-independent algorithms that solve ScheduleStream problems without any application-specific mechanisms. We apply ScheduleStream to Task and Motion Planning & Scheduling (TAMPAS), where we use GPU acceleration within samplers to expedite planning. We compare ScheduleStream algorithms to several ablations in simulation and find that they produce more efficient solutions. We demonstrate ScheduleStream on several real-world bimanual robot tasks at https://schedulestream.github.io.

翻译：双臂机器人和人形机器人因具备类似人类利用多臂高效完成任务的能力而备受关注。然而，由于混合离散-连续动作空间的维度增长，同时控制多臂在计算上极具挑战性。任务与运动规划（TAMP）算法虽能高效规划混合空间，但通常生成仅允许单臂依序运动的规划方案，而非支持双臂并行运动的时间调度。为将TAMP扩展至能够生成时间调度，我们提出ScheduleStream——首个面向采样操作实现规划与调度的通用框架。ScheduleStream采用混合持续动作建模时间动力学，此类动作可异步启动，其持续时长由参数函数决定。我们提出与领域无关的算法，无需任何应用特定机制即可求解ScheduleStream问题。我们将ScheduleStream应用于任务与运动规划及调度（TAMPAS），通过采样器中的GPU加速技术提升规划效率。在仿真实验中，我们将ScheduleStream算法与多种消融基准进行对比，结果表明其能生成更高效的解决方案。我们通过真实双臂机器人任务演示了ScheduleStream（演示视频见https://schedulestream.github.io）。