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. Code and supplementary material are available online at https://github.com/clear-nus/ltldog.

翻译：在复杂环境中有效运行并遵守特定约束，对于与人类交互并在人类周围操作的机器人实现安全且成功的部署至关重要。在本工作中，我们专注于在测试时生成符合新颖的静态与时态扩展约束/指令的长时程轨迹。我们提出了一个数据驱动的基于扩散的框架LTLDoG，该框架在给定使用有限线性时序逻辑（$\text{LTL}_f$）指定的指令时，修改反向过程的推理步骤。LTLDoG利用$\text{LTL}_f$上的满足度值函数，并通过其梯度场引导采样步骤。该值函数还可以被训练以泛化到训练期间未见过的新指令，从而实现灵活的测试时适应性。在机器人导航和操作中的实验表明，该方法能够生成满足指定避障和访问顺序公式的轨迹。代码和补充材料可在 https://github.com/clear-nus/ltldog 在线获取。