Organizations: Department of Computer, Control and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy
Agentic AI based on Large Language Model generalization capabilities offers a wide range of potential applications, including planning for embodied tasks. For example, embodied agents based on Foundation models can generate plausible plans in autonomous robotics scenarios. Due to limited context windows or hallucinatory phenomena in the next-token prediction formulation, behaviors may be generated without establishing whether the deployed robot and the observed environment actually support the requested operation, in what we call a "grounding failure". Thanks to the recent improvements in reasoning capabilities of foundation models, autonomous robot behavior generation problem can be formulated as a code generation problem. We present iAm.md, a Markdown standard and generation framework, that allows anchoring this process in complementary forms of deployment evidence. Through open-vocabulary semantic mapping, we combine local vision-language detections and object segmentation and refer them to persistent object records in this intermediate standardized representation, allowing agentic introspection. We then study this new technique on a simulated TIAGo, on navigation-and-manipulation tasks, showing how this standardized representation jointly supports skill self-assessment and executable task generalization.
Figures & tables
Figure 1: System architecture of the robot-description generation and embodied-agent execution stages. The generation agent extracts resolved robot evidence from the ROS 2 environment and records it in iAm.md . During task execution, the embodied agent combines this description with the semantic map and NL request, produces code for successive subgoals, and uses execution outcomes for introspective revision.
Table 1: (A) Phase 1 construction with and without iAm.md ; the condition without the document reports the effort before the 110-minute budget was reached. (B) The documented agent across construction (P1) and reuse (P2).
State Key Laboratory of Multimedia Information Processing, School of Computer2026 Science, Peking University · Institute for Brain and Intelligence, Fudan University · University of Science andJul Technology Beijing +2