InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866908783770337280 |
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| author | Ly, Kim Tien Lu, Kai Havoutis, Ioannis |
| author_facet | Ly, Kim Tien Lu, Kai Havoutis, Ioannis |
| contents | We introduce an interactive LLM-based framework designed to enhance the autonomy and robustness of domestic robots, targeting embodied intelligence. Our approach reduces reliance on large-scale data and incorporates a robot-agnostic pipeline that embodies an LLM. Our framework, InteLiPlan, ensures that the LLM's decision-making capabilities are effectively aligned with robotic functions, enhancing operational robustness and adaptability, while our human-in-the-loop mechanism allows for real-time human intervention when user instruction is required. We evaluate our method in both simulation and on the real robot platforms, including a Toyota Human Support Robot and an ANYmal D robot with a Unitree Z1 arm. Our method achieves a 95% success rate in the `fetch me' task completion with failure recovery, highlighting its capability in both failure reasoning and task planning. InteLiPlan achieves comparable performance to state-of-the-art LLM-based robotics planners, while using only real-time onboard computing. Project website: https://kimtienly.github.io/InteLiPlan. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14506 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy Ly, Kim Tien Lu, Kai Havoutis, Ioannis Robotics We introduce an interactive LLM-based framework designed to enhance the autonomy and robustness of domestic robots, targeting embodied intelligence. Our approach reduces reliance on large-scale data and incorporates a robot-agnostic pipeline that embodies an LLM. Our framework, InteLiPlan, ensures that the LLM's decision-making capabilities are effectively aligned with robotic functions, enhancing operational robustness and adaptability, while our human-in-the-loop mechanism allows for real-time human intervention when user instruction is required. We evaluate our method in both simulation and on the real robot platforms, including a Toyota Human Support Robot and an ANYmal D robot with a Unitree Z1 arm. Our method achieves a 95% success rate in the `fetch me' task completion with failure recovery, highlighting its capability in both failure reasoning and task planning. InteLiPlan achieves comparable performance to state-of-the-art LLM-based robotics planners, while using only real-time onboard computing. Project website: https://kimtienly.github.io/InteLiPlan. |
| title | InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.14506 |