A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning
A paper on arXiv (cs.AI) describes a decentralized navigation system for composite heterogeneous robots. Three robots share motion dynamics but use different LLM backends. Each robot combines an LLM policy agent, a UCB bandit, and a Double DQN controller. LLM inference is confined to round-level policy generation and refinement, not tick-level action selection. Robots communicate via a shared round summary. UCB performs refinement-mode selection, and the policy-conditioned Double DQN performs tick-level action selection. Four configurations were evaluated over 30 rounds. In the fixed simulation, the complete configuration reached the goal in all 90 correlated robot-round records and achieved the lowest median completion time (42 ticks) and P90 (73.2 ticks); its median was 25.0–39.1% lower than other configurations.
This research introduces a schema-bounded LLM approach for refining robot policies in decentralized multi-robot navigation. The system integrates LLM-based policy generation at the round level with UCB bandit refinement-mode selection and Double DQN tick-level control. The complete configuration achieved 100% goal-reaching in 90 robot-round records and the lowest median completion time (42 ticks) and P90 (73.2 ticks), outperforming other configurations by 25.0–39.1% in median completion time.
The key technical contribution is bounding LLM inference to round-level policy refinement, avoiding destabilization of local learning. The combination of UCB for refinement-mode selection and policy-conditioned Double DQN for tick-level actions enables stable integration of heterogeneous LLM backends. The reported performance improvement (25.0–39.1% lower median completion time) suggests that schema-bounded LLM guidance can enhance learning efficiency without disrupting local control loops.
This approach may enable practical deployment of LLM-guided policies in multi-robot systems where real-time LLM inference is infeasible. By limiting LLM involvement to higher-level policy updates, it reduces latency and computational cost while preserving local autonomy. The use of different LLM backends across robots indicates potential for heterogeneous fleets and vendor-agnostic integration.
The method could lower operational costs for robot fleets by reducing reliance on continuous LLM inference and enabling heterogeneous hardware/software stacks. Improved navigation efficiency (25–39% faster median completion) may translate to higher throughput in warehouse, delivery, or inspection applications. The decentralized architecture also reduces single points of failure and communication bottlenecks.
Next observable signals include replication of results with physical robots, extension to more complex environments, and comparison with centralized LLM planners. Further research may explore adaptive schema bounds and cross-LLM communication protocols. Commercial adoption could follow if the approach demonstrates robustness in real-world industrial or logistics settings.