# LLM-based Robot Task Planning with Exceptional Handling for General Purpose Service Robots

arXiv: https://arxiv.org/abs/2405.15646 · HTML: https://mlsanity.com/paper/2405.15646/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2024.

## Built on by (most cited first, 16 of 16)

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- [RoboReflect: A Robotic Reflective Reasoning Framework for Grasping Ambiguous-Condition Objects](https://mlsanity.com/paper/2501.09307/) (2025)
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- [Language-Guided Object Search in Agricultural Environments](https://mlsanity.com/paper/2503.01068/) (2025)
- [Semantic Intelligence: Integrating GPT-4 with A Planning in Low-Cost Robotics](https://mlsanity.com/paper/2505.01931/) (2025)
- [General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting](https://mlsanity.com/paper/2506.17462/) (2025)
- [PASG: A Closed-Loop Framework for Automated Geometric Primitive Extraction and Semantic Anchoring in Robotic Manipulation](https://mlsanity.com/paper/2508.05976/) (2025)
- [Chat with UAV -- Human-UAV Interaction Based on Large Language Models](https://mlsanity.com/paper/2512.08145/) (2025)
- [Secure Forgetting: A Framework for Privacy-Driven Unlearning in Large Language Model (LLM)-Based Agents](https://mlsanity.com/paper/2604.00430/) (2026)
- [Zero-shot adaptable task planning for autonomous construction robots: a comparative study of lightweight single and multi-AI agent systems](https://mlsanity.com/paper/2601.14091/) (2026)

## Abstract (arXiv)

The development of a general purpose service robot for daily life necessitates the robot's ability to deploy a myriad of fundamental behaviors judiciously. Recent advancements in training Large Language Models (LLMs) can be used to generate action sequences directly, given an instruction in natural language with no additional domain information. However, while the outputs of LLMs are semantically correct, the generated task plans may not accurately map to acceptable actions and might encompass various linguistic ambiguities. LLM hallucinations pose another challenge for robot task planning, which results in content that is inconsistent with real-world facts or user inputs. In this paper, we propose a task planning method based on a constrained LLM prompt scheme, which can generate an executable action sequence from a command. An exceptional handling module is further proposed to deal with LLM hallucinations problem. This module can ensure the LLM-generated results are admissible in the current environment. We evaluate our method on the commands generated by the RoboCup@Home Command Generator, observing that the robot demonstrates exceptional performance in both comprehending instructions and executing tasks.