A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
- URL: http://arxiv.org/abs/2410.22997v2
- Date: Wed, 06 Nov 2024 16:57:03 GMT
- Title: A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics
- Authors: Jonas Bode, Bastian Pätzold, Raphael Memmesheimer, Sven Behnke,
- Abstract summary: We compare prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics.
We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models.
- Score: 16.064583670720587
- License:
- Abstract: Recent advances in LLM have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLM to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models.
Related papers
- MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [52.739500459903724]
Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation.
We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents.
We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
arXiv Detail & Related papers (2024-11-26T17:53:44Z) - COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems.
A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots.
The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
arXiv Detail & Related papers (2024-09-23T15:53:41Z) - Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model [6.9268843428933025]
Large language models (LLMs) have demonstrated powerful planning and reasoning capabilities for comprehension and processing of semantic information.
We propose a novel language-model based framework that enables robots to autonomously plan behaviors and low-level execution under given textual instructions.
arXiv Detail & Related papers (2024-08-15T17:33:32Z) - Interactive Planning Using Large Language Models for Partially
Observable Robotics Tasks [54.60571399091711]
Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks.
We present an interactive planning technique for partially observable tasks using LLMs.
arXiv Detail & Related papers (2023-12-11T22:54:44Z) - TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation.
Specifically, task decomposition, tool selection, and parameter prediction are assessed.
Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
arXiv Detail & Related papers (2023-11-30T18:02:44Z) - TPTU: Large Language Model-based AI Agents for Task Planning and Tool
Usage [28.554981886052953]
Large Language Models (LLMs) have emerged as powerful tools for various real-world applications.
Despite their prowess, intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks.
This paper proposes a structured framework tailored for LLM-based AI Agents.
arXiv Detail & Related papers (2023-08-07T09:22:03Z) - LEMMA: Learning Language-Conditioned Multi-Robot Manipulation [21.75163634731677]
LanguagE-Conditioned Multi-robot MAnipulation (LEMMA)
LeMMA features 8 types of procedurally generated tasks with varying degree of complexity.
For each task, we provide 800 expert demonstrations and human instructions for training and evaluations.
arXiv Detail & Related papers (2023-08-02T04:37:07Z) - Optimal task and motion planning and execution for human-robot
multi-agent systems in dynamic environments [54.39292848359306]
We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks.
The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task.
We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
arXiv Detail & Related papers (2023-03-27T01:50:45Z) - ProgPrompt: Generating Situated Robot Task Plans using Large Language
Models [68.57918965060787]
Large language models (LLMs) can be used to score potential next actions during task planning.
We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
arXiv Detail & Related papers (2022-09-22T20:29:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.