Zero-Shot Embodied Decision Making with Structured Prior Knowledge and Adaptive Calibration in ALFWorld

Abstract

Large-language-model (LLM) agents perform well in embodied benchmarks but are costly, stochastic, and difficult to audit. We propose an LLM-free zero-shot decision agent for ALFWorld that combines structured commonsense priors with adaptive calibration. The agent uses object-location priors, task templates, synonym mappings, and a hierarchical state controller over admissible commands. Because every decision is traceable to explicit knowledge entries, success and failure signals update only the responsible entries rather than all parameters. On 134 ALFWorld valid_unseen tasks, static priors obtain 67.2% success; symmetric calibration over P(obj,loc), M(target,entity), and S(word) improves this to 73.9%, with one-round convergence and CPU-only execution. We also observe that 34/134 tasks contain description-environment inconsistencies; on the consistent subset, our system reaches 93.0%. The results show that interpretable structured priors can be a practical alternative for well-specified embodied decision making.