A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning
This paper addresses navigation by composite heterogeneous robots in a decentralized system when policy reasoning and local control operate at different update levels. In a NetLogo--Python implementation, three robots share motion dynamics but use different LLM backends. Each robot independently combines a large language model (LLM) policy agent, an Upper Confidence Bound (UCB) bandit, and a Double Deep Q-Network (Double DQN) controller; no central LLM generates team actions. LLM inference is confined to round-level policy generation and refinement rather than tick-level action selection. The robots perform cross-LLM communication through a shared round summary containing policies, outcom...
| Authors | Chongwen Dong, Mithun Paul Saint-Germain, Pinjari Asif et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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