Operating effectively in complex environments while complying with specified
constraints is crucial for the safe and successful deployment of robots that
interact with and operate around people. In this work, we focus on generating
long-horizon trajectories that adhere to novel static and temporally-extended
constraints/instructions at test time. We propose a data-driven diffusion-based
framework, LTLDoG, that modifies the inference steps of the reverse process
given an instruction specified using finite linear temporal logic
($\text{LTL}_f$). LTLDoG leverages a satisfaction value function on
$\text{LTL}_f$ and guides the sampling steps using its gradient field. This
value function can also be trained to generalize to new instructions not
observed during training, enabling flexible test-time adaptability. Experiments
in robot navigation and manipulation illustrate that the method is able to
generate trajectories that satisfy formulae that specify obstacle avoidance and
visitation sequences.

基于有限线性时态逻辑（LTLf）的指令，本研究提出了一种数据驱动的扩散式框架（LTLDoG），用于在复杂环境中生成遵守新的静态和时间延伸的约束 / 指令的远景轨迹，并在机器人导航和操纵的实验中验证了其有效性。

LTLDoG：满足时间延伸的符号约束的安全扩散规划

LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe  Diffusion-based Planning

Trajectory generation is an important task in movement studies; it
circumvents the privacy, ethical, and technical challenges of collecting real
trajectories from the target population. In particular, real trajectories in
the wildlife domain are scarce as a result of ethical and environmental
constraints of the collection process. In this paper, we consider the problem
of generating long-horizon trajectories, akin to wildlife migration, based on a
small set of real samples. We propose a hierarchical approach to learn the
global movement characteristics of the real dataset and recursively refine
localized regions. Our solution, WildGraph, discretizes the geographic path
into a prototype network of H3 (this https URL) regions and
leverages a recurrent variational auto-encoder to probabilistically generate
paths over the regions, based on occupancy. WildGraph successfully generates
realistic months-long trajectories using a sample size as small as 60.
Experiments performed on two wildlife migration datasets demonstrate that our
proposed method improves the generalization of the generated trajectories in
comparison to existing work while achieving superior or comparable performance
in several benchmark metrics. Our code is published on the following
repository: \url{this https URL}.

研究了利用一小组真实样本生成类似野生动物迁徙的长期轨迹的问题，提出了一种分层方法来学习真实数据集的全局运动特征并递归改进本地化区域，通过使用 H3 区域的原型网络和经验变分自编码器基于占用概率，我们的解决方案 WildGraph 成功地生成了具有现实感的长达数月的轨迹。在两个野生动物迁徙数据集上进行的实验表明，与现有工作相比，我们提出的方法在生成轨迹的泛化能力上取得了改进，同时在几项基准指标上实现了更高或相当的性能。