Abstract
Robotic agents are tasked with mastering common sense and making long-term sequential decisions to execute daily tasks based on natural language instructions. Recent advancements in Large Language Models (LLMs) have catalyzed efforts for complex robotic planning. However, despite their superior generalization and comprehension capabilities, LLM task plans sometimes suffer from issues of accuracy and feasibility. To address these challenges, we propose RoboGPT \footnote{For more details, please refer to our project page \href{https://github.com/Cwb0106/RoboGPT-}{ https://github.com/Cwb0106/RoboGPT}.
Original language | English |
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Journal | IEEE Transactions on Cognitive and Developmental Systems |
Publication status | Submitted - 2024 |
Keywords
- Embodied planning
- Large language model
- Embodied AI