Task and Motion Planning with Large Language Models for Object Rearrangement (2303.06247v4)

Abstract: Multi-object rearrangement is a crucial skill for service robots, and commonsense reasoning is frequently needed in this process. However, achieving commonsense arrangements requires knowledge about objects, which is hard to transfer to robots. LLMs are one potential source of this knowledge, but they do not naively capture information about plausible physical arrangements of the world. We propose LLM-GROP, which uses prompting to extract commonsense knowledge about semantically valid object configurations from an LLM and instantiates them with a task and motion planner in order to generalize to varying scene geometry. LLM-GROP allows us to go from natural-language commands to human-aligned object rearrangement in varied environments. Based on human evaluations, our approach achieves the highest rating while outperforming competitive baselines in terms of success rate while maintaining comparable cumulative action costs. Finally, we demonstrate a practical implementation of LLM-GROP on a mobile manipulator in real-world scenarios. Supplementary materials are available at: https://sites.google.com/view/LLM-grop

• The paper introduces LLM-GROP, which integrates commonsense LLM reasoning with task and motion planning to achieve semantically valid object arrangements.
• It extracts symbolic spatial relationships via structured prompting and generates geometric configurations using Gaussian and rejection sampling techniques.
• Experimental results demonstrate that LLM-GROP outperforms baselines by improving user ratings and task execution efficiency in object rearrangement tasks.

Task and Motion Planning with LLMs for Object Rearrangement

This paper introduces LLM-GROP, a method that combines LLMs with task and motion planning (TAMP) for semantically valid object rearrangement tasks performed by service robots. The primary objective is to leverage the commonsense reasoning capabilities of LLMs to perform tableware object arrangements based on semantically valid configurations, addressing deficiencies in current robotic systems that often struggle with such high-level reasoning tasks.

Methodology Overview

LLM-GROP is designed to bridge the gap between natural language processing and robotic task execution by utilizing LLMs to infer spatial relationships among objects and employing task and motion planning to execute object rearrangements. The methodology consists of two main components:

1. Symbolic Spatial Relationships: The method employs LLMs to extract symbolic spatial relationships between objects through a structured prompting technique. This involves a predefined template to extract relationships like "to the left of" or "on top of." To ensure logical consistency and avoid contradictory arrangements, logical reasoning is integrated using Answer Set Programming (ASP) for recursive reasoning and verification of logical constraints.
2. Geometric Spatial Relationships: After establishing symbolic relationships, LLM-GROP generates feasible geometric configurations based on these symbolic instructions. This is achieved through Gaussian sampling and rejection sampling techniques, ensuring the sampled positions respect constraints such as non-overlapping objects and staying within table boundaries.
3. Task-Motion Planning: Once geometric configurations are available, LLM-GROP utilizes TAMP to compute efficient and feasible navigation and manipulation plans. This involves determining optimal navigation goals and executing tasks to maximize long-term utility, considering the feasibility and efficiency of rearrangement plans.

Experimental Results

The evaluation of LLM-GROP involves comparing it to three baselines across a variety of object rearrangement tasks. The baselines range from simple task planning with random arrangements to more sophisticated approaches like GROP. Key findings from experiments indicate that LLM-GROP consistently achieves higher user ratings for arrangement quality while maintaining or improving task execution efficiency. This demonstrates the advantage of integrating LLM-derived commonsense knowledge with robotic planning.

Implications and Future Directions

The LLM-GROP framework highlights the potential for LLMs to address challenges in robotic task planning by providing valuable commonsense reasoning capabilities. By integrating these models with traditional robotic techniques, robots can effectively perform complex tasks that require human-like understanding of object relationships and spatial arrangements.

The successful demonstration on both simulated and real-world platforms underscores the practical viability of LLM-GROP. As LLMs continue to evolve, their application in robotics could be expanded to encompass a wider range of domains, potentially improving robots' ability to autonomously handle more complex and dynamic environments. Future work could focus on integrating perception-based methods with LLM-GROP to handle unknown objects and environments, extending the model's predictive capabilities beyond predefined scenarios.

In conclusion, this research sets a foundation for further exploration into the intersection between LLMs and robotics, offering a promising approach to enhance robots' ability to execute tasks requiring high-level reasoning and adaptability in diverse contexts.