AutoTAMP: Autoregressive Task and Motion Planning with LLMs

Core Concepts

Using large language models for translating natural language task descriptions to formal task specifications improves task success rates in complex environments.

Abstract

The content discusses the use of Large Language Models (LLMs) for translating natural language instructions into formal task specifications for robots. The approach involves autoregressive translation from high-level task descriptions to an intermediate representation, Signal Temporal Logic (STL), which is then used by a Task-and-Motion Planning (TAMP) algorithm. By addressing syntactic and semantic errors through re-prompting techniques, the method outperforms direct LLM planning in challenging 2D task domains. A comprehensive experimental evaluation across various scenarios demonstrates the effectiveness of the proposed AutoTAMP framework.
I. Introduction

Effective human-robot interaction requires understanding, planning, and executing complex tasks.
Recent advances in Large Language Models (LLMs) show promise for translating natural language into robot action sequences.
Existing approaches face challenges with complex environmental and temporal constraints.
II. Problem Description

Conversion of natural language instructions into motion plans encoded as timed waypoints.
Environment state described by named obstacles provided as additional context.
Generation of constraint-satisfying trajectories based on instructions and environment state.
III. Methods

Comparison of different approaches using LLMs for task planning.
Introduction of AutoTAMP framework for translating NL to STL and planning trajectories.
Utilization of re-prompting techniques for correcting syntax and semantic errors.
IV. Experimental Design

Evaluation across single-agent and multi-agent scenarios with varying constraints.
Impact assessment of error correction on translation performance.
Integration of NL2TL model for comparison with pre-trained LLMs.
V. Results

Task success rates compared across different methods using GPT-3 and GPT-4 as LLMs.
Failures analyzed based on execution time violations, action sequencing issues, and translation errors.
VI. Related Work

Overview of related research on combined task and motion planning, LLMs in TAMP, and NL-to-task representation mapping.
VII. Conclusion

AutoTAMP framework shows improved performance over direct LLM planning in handling complex geometric and temporal constraints.

Stats

"We show that our approach outperforms several methods using LLMs as planners in complex task domains."
"We conduct an ablation study over the translation step by integrating a fine-tuned NL-to-STL model."
"The cost of planning time is high, especially when there are multiple iterations of re-prompting."

Quotes

"We conclude that in-context learning with pre-trained LLMs is well suited for language-to-task-specification translation."
"Our work addresses some limitations of prior approaches."
"Translation with no error correction has modest success across task scenarios."

Key Insights Distilled From

AutoTAMP

by Yongchao Che... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2306.06531.pdf

Deeper Inquiries

How can the AutoTAMP framework be adapted to handle manipulation tasks

To adapt the AutoTAMP framework for manipulation tasks, several adjustments can be made. Firstly, the translation process from natural language to Signal Temporal Logic (STL) should consider actions relevant to manipulation, such as grasping, lifting, and placing objects. The STL representation needs to capture these specific manipulative actions and constraints accurately.
Secondly, incorporating a planner that is tailored for manipulation tasks is crucial. This planner should understand the dynamics of manipulating objects in the environment and optimize trajectories considering physical constraints like object weight, friction, and collision avoidance.
Additionally, semantic error correction during translation becomes even more critical for manipulation tasks due to their intricacies. Ensuring that the translated task specifications align with the intended manipulative actions will enhance task success rates significantly.
Lastly, integrating a feedback loop between the execution of manipulative actions and the high-level task planning can improve overall performance. This feedback mechanism can help refine plans based on real-world outcomes during execution.

What are the implications of incorporating NL2TL model compared to fine-tuning a pre-trained LLM

Incorporating NL2TL model into AutoTAMP compared to fine-tuning a pre-trained Large Language Model (LLM) offers some distinct advantages.
Using NL2TL provides a specialized model specifically trained for translating natural language instructions into temporal logic expressions efficiently. This targeted training enhances accuracy in converting complex linguistic descriptions into formal task specifications like Signal Temporal Logic (STL).
Moreover, NL2TL reduces dependency on extensive data or additional training by leveraging pre-existing knowledge within its architecture. It streamlines the translation process while maintaining high accuracy levels comparable to fine-tuned LLMs.
By combining NL2TL with AutoTAMP's error correction mechanisms for syntactic and semantic errors during translation ensures robustness in handling various types of input instructions effectively.

How can the runtime efficiency of formal planners be improved while using large language models

Improving runtime efficiency of formal planners while using large language models involves several strategies:

Optimized Search Algorithms: Implementing efficient search algorithms within formal planners can reduce computational complexity when exploring possible action sequences or trajectory optimizations based on STL specifications.

Parallel Processing: Utilizing parallel processing capabilities can distribute computation across multiple cores or machines simultaneously, speeding up planning processes significantly.

Caching Mechanisms: Implementing caching mechanisms within planners can store intermediate results or solutions obtained during planning iterations for reuse in similar scenarios without recalculating them each time.

Reduced State Space Exploration: Limiting unnecessary exploration of vast state spaces by focusing on relevant areas based on context provided by LLMs helps streamline planning procedures without compromising solution quality.

Hardware Acceleration: Leveraging hardware acceleration techniques such as GPU computing or specialized processors optimized for certain operations involved in planning tasks can boost overall runtime performance.