EvoPlan: Evolutionary Neuro-Symbolic Robot Planning with Spatio-Temporal Guarantees
Overview
This paper introduces EvoPlan, a neuro-symbolic framework that combines evolutionary search with Signal Temporal Logic constraints to ensure plan validity and safety for robot navigation and manipulation tasks. The system includes three components: an offline procedure that mines global mobility constraints from demonstrations, an evolutionary PDDL planner that iteratively repairs LLM-generated plans using programmatic validators, and a constrained execution loop that grounds plans in Nav2 costmaps and updates replanning upon constraint violations. Demonstrations include Bench2Drive, HA-VLN-CE, ALFWorld, NL-PDDL Blocksworld, Gazebo simulations, and physical tests on a Clearpath Jackal robot.
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- arXiv Robotics — research abstractsSignalEvoPlan: Evolutionary Neuro-Symbolic Robot Planning with Spatio-Temporal Guarantees
This paper introduces EvoPlan, a neuro-symbolic framework that combines evolutionary search with Signal Temporal Logic constraints to ensure plan validity and safety for robot navigation and manipulation tasks. The system includes three components: an offline procedure that mines global mobility constraints from demonstrations, an evolutionary PDDL planner that iteratively repairs LLM-generated plans using programmatic validators, and a constrained execution loop that grounds plans in Nav2 costmaps and updates replanning upon constraint violations. Demonstrations include Bench2Drive, HA-VLN-CE, ALFWorld, NL-PDDL Blocksworld, Gazebo simulations, and physical tests on a Clearpath Jackal robot.
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