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arXiv AI By Maitrey Gramopadhye, Prakash Baskaran, Xiao Liu, Songpo Li, Soshi Iba

STEP: State-Aware Task Estimation and Planning with Multi-Modal LLMs for Human-Robot Collaboration

Read the original on arXiv AI →

The paper introduces STEP, a State‑Aware Task Estimator and Planner that uses multi‑modal large language models to explicitly estimate system states and predict state transitions during task planning. By forecasting future states alongside actions, STEP reduces hallucinated actions and improves task‑convergent planning. In a simulated robot assembly task, STEP outperforms the state‑of‑the‑art by 32.8% in action executability and 14.8% in final‑state error.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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