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。