# InteLiPlan: An Interactive Lightweight LLM-Based Planner for Domestic Robot Autonomy

arXiv: https://arxiv.org/abs/2409.14506 · HTML: https://mlsanity.com/paper/2409.14506/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2024.

## Built on by (most cited first, 5 of 5)

- [LightPlanner: Unleashing the Reasoning Capabilities of Lightweight Large Language Models in Task Planning](https://mlsanity.com/paper/2503.08508/) (2025)
- [Vision-Language-Policy Model for Dynamic Robot Task Planning](https://mlsanity.com/paper/2512.19178/) (2025)
- [TransLLM: A Unified Multi-Task Foundation Framework for Urban Transportation via Learnable Prompting](https://mlsanity.com/paper/2508.14782/) (2025)
- [A Hierarchical Error-Corrective Graph Framework for Autonomous Agents with LLM-Based Action Generation](https://mlsanity.com/paper/2603.08388/) (2026)
- [When Should a Failing Robot Ask? Initiating Corrective Human-Robot Dialogue from Audited Sensor Evidence](https://mlsanity.com/paper/2609.21942/) (2026)

## Abstract (arXiv)

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.