Robotic planning breaks down when commonsense reasoning is required to resolve linguistic ambiguity and to interpret objects correctly. To address this, we present ConceptBot, a modular planning framework that integrates large language models with knowledge graphs to produce feasible, risk-aware plans while jointly disambiguating instructions and grounding object semantics.

ConceptBot comprises three components: (i) an Object Properties Extraction (OPE) module that augments scene understanding with semantic concepts from ConceptNet; (ii) a User Request Processing (URP) module that resolves ambiguities and structures free-form instructions; and (iii) a Planner that synthesizes context-aware, feasible pick-and-place policies. Evaluations in simulation and on real-world setups show consistent gains over prior LLM-based planners—for example, +56 percentage points on implicit tasks (87\% vs. 31\% for SayCan) and +61 points on risk-aware tasks (76\% vs. 15\%)—and an overall score of 80\% on SafeAgentBench. These improvements translate to more reliable performance in unstructured environments without domain-specific training.