{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ltldog-satisfying-temporally-extended","title":"LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe Diffusion-based Planning","arxiv_id":"2405.04235","date":"2024-05-07","proceeding":null,"authors":["Zeyu Feng","Hao Luan","Pranav Goyal","Harold Soh"],"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 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.","url_abs":"https://arxiv.org/abs/2405.04235v2","url_pdf":"https://arxiv.org/pdf/2405.04235v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ltldog-satisfying-temporally-extended","repo_url":"https://github.com/clear-nus/ltldog","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"offline-rl","task_name":"Offline RL"},{"task_slug":"robot-manipulation","task_name":"Robot Manipulation"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"},{"task_slug":"trajectory-planning","task_name":"Trajectory Planning"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.04235","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.04235"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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