STEP: State-Aware Task Estimation and Planning with Multi-Modal LLMs for Human-Robot Collaboration
Effective human-robot collaboration in industrial settings requires robots to understand human intentions and assist with task planning, reducing workload. Recent works have explored the use of Multi-modal Large Language Models (MM-LLMs) for task planning in such data-scarce scenarios, leveraging in-context learning to interpret user actions and generate long-horizon action plans in natural language. However, MM-LLMs inherently lack an understanding of system states and do not track state transitions, often leading to hallucinated actions that deviate from the intended goal. Additionally, generating action plans in natural language tends to limit the generated plans to a high level, intro...
| Authors | Maitrey Gramopadhye, Prakash Baskaran, Xiao Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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