Generative Partially Specified Finite State Machine for Robot Behavior Planning
Researchers introduce a Generative Partially Specified Finite State Machine (GPSFSM) neurosymbolic architecture for robot behavior planning, combining the structure of FSMs with large language models. The system demonstrates higher plan-generation success rates than the state-of-the-art BTGenBot, especially in zero-shot scenarios, while maintaining comparable or lower planning latency. An open-source ROS2 stack implementing this approach is released.
Why it matters: This work presents the first generative FSM framework for robotics, offering a computationally efficient and interpretable alternative to existing behavior planning methods.
Full story at: arXiv Robotics ↗