Efficient Long-Term Robot Task Planning using Large Language Models and Scene Graphs

Core Concepts

A novel approach called DELTA leverages the power of scene graphs and large language models to decompose long-term robot task goals into a sequence of sub-goals, enabling efficient and automated task planning with higher success rates and significantly shorter planning times compared to the state of the art.

Abstract

The paper proposes a novel approach called DELTA (Decomposed Efficient Long-Term TAsk planning for mobile robots using Large Language Models) to solve long-term robot task planning problems. DELTA combines the use of large language models (LLMs) and structured 3D scene graph representations of the environment. The key steps of DELTA are: Domain Generation: DELTA uses an LLM to generate a PDDL domain file describing the available actions and their preconditions and effects. Scene Graph Pruning: DELTA prunes the 3D scene graph to only include the relevant objects for the given task, reducing the complexity of the planning problem. Problem Generation: DELTA uses the LLM to generate a PDDL problem file based on the pruned scene graph and the task goal. Goal Decomposition: DELTA leverages the LLM to decompose the long-term task goal into a sequence of sub-goals, which can be solved more efficiently by an automated task planner. Autoregressive Sub-Task Planning: DELTA solves the sub-problems autoregressively using an automated task planner, concatenating the individual sub-plans into a final plan. The evaluation shows that DELTA, especially when using the GPT-4-turbo LLM, significantly outperforms baseline approaches in terms of success rate, plan quality, and planning time across various long-term task planning domains and environments. The key advantages of DELTA are the efficient grounding of natural language into formal planning language, the structured scene graph representation, and the goal decomposition strategy.

Stats

The robot can only load one item at a time. The scene graphs contain a hierarchical structure with three layers: floor, room, and item layers. The scene graphs used in the experiments have between 7 to 12 rooms and 16 to 42 items.

Quotes

"For robots solving long-term task sequences in large and complex environments, having efficient environment representations is a crucial prerequisite for the robot to understand the semantic information." "Utilizing LLMs and automated task planning techniques to solve long-term robot task planning problems, with structured representations of large environments, still remains an open research topic."

Key Insights Distilled From

DELTA

by Yuchen Liu,L... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03275.pdf

Deeper Inquiries

How could DELTA be extended to handle dynamic environments where the scene graph representation changes over time?

In order to handle dynamic environments with changing scene graph representations, DELTA could be extended by incorporating real-time perception and updating mechanisms. This would involve integrating sensors or modules that can detect changes in the environment and update the scene graph accordingly. Additionally, the system could implement algorithms for incremental scene graph updates, allowing for efficient and timely adjustments as the environment evolves. By continuously monitoring and adapting to changes in the scene graph, DELTA can ensure that the task planning remains accurate and effective in dynamic environments.

What are the potential limitations of using LLMs for task planning, and how could these be addressed in future work?

One potential limitation of using LLMs for task planning is their tendency to generate incorrect or infeasible plans, especially in complex scenarios with detailed spatial relationships. This can lead to failures in plan execution and inefficiencies in task completion. To address this, future work could focus on enhancing the LLMs' understanding of spatial reasoning and action feasibility. This could involve training the models on a wider range of tasks and environments to improve their generalization capabilities. Additionally, incorporating feedback mechanisms to validate generated plans and refine them based on real-world outcomes can help improve the accuracy and reliability of LLM-based task planning systems.

How could the goal decomposition strategy in DELTA be further improved to handle even more complex long-term tasks?

To enhance the goal decomposition strategy in DELTA for handling more complex long-term tasks, several approaches can be considered. One way is to introduce hierarchical decomposition techniques that break down overarching goals into sub-goals at multiple levels of abstraction. This hierarchical structure can help manage the complexity of long-term tasks by dividing them into more manageable sub-problems. Additionally, incorporating reinforcement learning methods to guide the goal decomposition process based on past experiences and task performance can improve the efficiency and effectiveness of the decomposition strategy. Furthermore, integrating domain-specific knowledge and constraints into the goal decomposition process can ensure that the sub-goals are generated in a way that aligns with the task requirements and constraints, leading to more robust and successful task planning outcomes.