InteLiPlan: an interactive lightweight LLM-based planner for domestic robot autonomy

Journal article

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 LLMs 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.

Authors: Ly, KT; Lu, K; Havoutis, I

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Electrical and Electronics Engineers
• Journal: IEEE Robotics and Automation Letters
• Volume: 11
• Issue: 3
• Pages: 3875-3882
• Publication date: 2026-02-09
• Acceptance date: 2026-01-08
• DOI: 10.1109/lra.2026.3662577
• EISSN: 2377-3766
• Language: English
• Keywords: robots; planning; real-time systems; visualization; cognition; robustness; power capacitors; decision making; safety; pipelines