让家用机器人用小模型实现智能规划，还能实时听人指挥。
InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy
- 用轻量级大模型做机器人任务规划，不依赖海量数据。
- 在真实机器人上完成95%的取物任务，能自动修复失败。
- 适合想低成本提升机器人自主能力的研发者使用。
我们提出一个交互式轻量级大模型框架InteLiPlan，旨在提升家用机器人的具身智能与鲁棒性。该方法减少对大规模数据的依赖，采用通用机器人架构，将大模型决策能力与机器人功能有效对齐，增强操作鲁棒性与适应性。通过人机协同机制，在用户需要时支持实时干预。我们在仿真环境及真实机器人平台（包括丰田人形支持机器人、ANYmal D 搭载 Unitree Z1 机械臂）上评估该方法，结果显示在「取物」任务中成功率高达95%，具备出色的故障推理与任务规划能力。InteLiPlan性能接近当前顶尖大模型机器人规划系统，且仅需实时本地计算，无需外部算力支持。
原文摘要 · Abstract (English)
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.
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