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

arXiv:2405.04235v1 Announce Type: cross
Abstract: 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 …

abstract arxiv constraints cs.lg cs.ro deployment diffusion environments focus horizon novel people planning robots safe test type while work