EvoPlan: Evolutionary Neuro-Symbolic Robot Planning with Spatio-Temporal Guarantees
EvoPlan is a neuro-symbolic framework combining LLM fluency and classical PDDL planner guarantees. It has three parts: offline mining of global STL constraints from demonstrations, an evolutionary PDDL planner, and a constrained execution loop. All LLM calls use local open-weight models, enabling on-robot deployment without cloud dependency. Tested on Bench2Drive, HA-VLN-CE, and ALFWorld benchmarks.
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[Submitted on 7 Jul 2026]
Title:EvoPlan: Evolutionary Neuro-Symbolic Robot Planning with Spatio-Temporal Guarantees
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Abstract:LLM-based robot planners are fluent but cannot guarantee that their plans are executable or safe. Classical PDDL planners can guarantee these properties, but only after the problem is fully specified, and they make poor use of an LLM's ability to read context and repair plans. This paper presents a neuro-symbolic framework with three parts. All LLM calls use a locally-hosted open-weight model, so the pipeline can be deployed on-robot with no cloud dependency. First, an offline procedure that mines a single global Signal Temporal Logic (STL) constraint on mobility from demonstration data. The procedure recovers codified rules (e.g., stopping at red lights, mined from nuPlan driving logs) or population preferences (e.g., social-navigation comfort, mined from SCAND teleoperation), depending on what the demonstrations encode. Because the demonstrations are a one-class signal, we generate the missing negatives with counterfactual perturbations and an LLM violation generator and then fit the constraint by evolutionary search. We use the mined constraint to shield a vision-language driving policy on Bench2Drive and two discrete-action navigation policies on HA-VLN-CE. Second, an evolutionary PDDL planner: an LLM proposes and repairs plans, programmatic validators decide which ones survive, and the validated portion of the plan grows over iterations. We test the planner on the open-world ALFWorld Text benchmark, where it beats strong baselines and stays robust when the goal vocabulary does not match the action-model vocabulary. Third, a constrained execution loop: the planner's plan is compiled into waypoints, the waypoints are checked against the mined constraint, and the planner re-plans on a violation. We illustrate the full pipeline via demonstrations using the Gazebo simulator.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.06724 [cs.RO]
(or arXiv:2607.06724v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.06724
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Bhavya Sai Nukapotula [view email] [v1] Tue, 7 Jul 2026 18:42:59 UTC (1,990 KB)
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