Embodied Task Planning via Graph-Informed Action Generation with Large Language Models

In three linesGiG, a planning framework for embodied agents, uses Graph-in-Graph architecture with GNN to encode environmental states and structure experience memory. A bounded lookahead module enhances planning via symbolic transition logic. Evaluated on Robotouille and ALFWorld, GiG outperforms baselines with +22% to +37% Pass@1 gains.

Summary generated by Claude — human-verified