Safe and successful deployment of robots requires not only the ability to generate complex plans but also the capacity to frequently replan and correct execution errors. This paper addresses the challenge of long-horizon trajectory planning under temporally extended objectives in a receding horizon manner. To this end, we propose DOPPLER, a data-driven hierarchical framework that generates and updates plans based on instruction specified by linear temporal logic (LTL). Our method decomposes temporal tasks into chain of options with hierarchical reinforcement learning from offline non-expert datasets. It leverages diffusion models to generate options with low-level actions. We devise a determinantal-guided posterior sampling technique during batch generation, which improves the speed and diversity of diffusion generated options, leading to more efficient querying. Experiments on robot navigation and manipulation tasks demonstrate that DOPPLER can generate sequences of trajectories that progressively satisfy the specified formulae for obstacle avoidance and sequential visitation. Demonstration videos are available online at: https://philiptheother.github.io/doppler/.

翻译：机器人的安全与成功部署不仅需要生成复杂规划的能力，还要求具备频繁重规划与纠正执行误差的容量。本文针对在滚动时域框架下，处理具有时序扩展目标的长程轨迹规划问题。为此，我们提出DOPPLER——一种数据驱动的分层框架，该框架基于线性时序逻辑（LTL）指定的指令生成并更新规划。我们的方法利用分层强化学习，将时序任务分解为选项链，并利用离线非专家数据集进行学习。它借助扩散模型生成包含底层动作的选项。我们设计了一种行列式引导的后验采样技术，用于在批次生成过程中提升扩散生成选项的速度与多样性，从而实现更高效的查询。在机器人导航与操作任务上的实验表明，DOPPLER能够生成逐步满足障碍物规避与顺序访问等指定公式的轨迹序列。演示视频可在以下网址获取：https://philiptheother.github.io/doppler/。