iAm.md: Robot Skill Self-Assessment through Agentic Introspection for Unknown Open-Vocabulary Domains
Agentic AI based on Large Language Model generalization capabilities offers a wide range of potential applications, including planning for embodied tasks.
Key points
- For example, embodied agents based on Foundation models can generate plausible plans in autonomous robotics scenarios.
- 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.
Sources (1)
- [1]iAm.md: Robot Skill Self-Assessment through Agentic Introspection for Unknown Open-Vocabulary DomainsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 10:28 PM
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.
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