LLM-State: An Open World State Representation for Long-horizon Task Planning with Large Language Models

Основные понятия

The proposed LLM-State representation enables continuous expansion and updating of object attributes from the LLM's inherent capabilities for context understanding and historical action reasoning, allowing robust retrospective summary of the sequence of actions and enhancing long-horizon task planning in open-world environments.

Аннотация

The authors address the problem of long-horizon task planning with Large Language Models (LLMs) in open-world household environments. Existing methods fail to explicitly track key objects and attributes, leading to erroneous decisions in long-horizon tasks, or rely on highly engineered state features and feedback, which is not generalizable.
The authors propose a novel state representation called LLM-State that combines a structured object-centric representation with an unstructured summary of historical actions and observed failures. The structured object-centric representation describes the world as a list of objects, each with different attributes indicating their current status. The unstructured summary enables better prediction of object attributes via a step-wise breakdown and chain-of-thought process, and also captures recent failed robot actions and reasons about their possible causes.
The LLM-State representation is constructed and updated automatically by the LLM, providing continuous expansion and updating of object attributes based on the LLM's inherent capabilities for context understanding and historical action reasoning. This allows the LLM to maintain a comprehensive record of an object's attributes and changes, enabling robust retrospective summary of the sequence of actions leading to the current state and enhancing the LLM's decision-making in long-horizon task planning.
The authors validate their approach through experiments across simulated and real-world task planning scenarios, demonstrating significant improvements over baseline methods in a variety of tasks requiring long-horizon state tracking and reasoning.

Статистика

"The robot takes the food from the fridge, puts it in the microwave and uses the microwave. The food is now warmed up."
"The robot opens the fridge and picks up the food. The fridge remains open."

Цитаты

"Our proposed representation maintains a comprehensive record of an object's attributes and changes, enabling robust retrospective summary of the sequence of actions leading to the current state."
"The unstructured summary enables better prediction of object attributes via a step-wise breakdown and chain-of-thought process, and also captures recent failed robot actions and reasons about their possible causes."

Ключевые выводы из

LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Model

by Siwei Chen,A... в arxiv.org 04-23-2024

https://arxiv.org/pdf/2311.17406.pdf

Дополнительные вопросы

How can the LLM-State representation be extended to capture more complex object relationships and interactions?

The LLM-State representation can be extended to capture more complex object relationships and interactions by incorporating graph-based structures to represent object dependencies and interactions. This can involve creating nodes for each object and edges to denote relationships such as containment, spatial proximity, or functional dependencies. By utilizing graph representations, the LLM-State can capture not only individual object attributes but also the connections and dependencies between objects in the environment.
Additionally, the representation can be enhanced by incorporating temporal information to track object interactions over time. This can involve maintaining a history of object states and interactions, allowing the LLM to reason about how objects have changed and interacted with each other throughout a sequence of actions. By incorporating temporal dynamics, the LLM-State can better capture the evolution of object relationships and interactions in dynamic environments.
Furthermore, the LLM-State representation can benefit from incorporating uncertainty modeling to handle ambiguous or incomplete information about object relationships. By integrating probabilistic reasoning or uncertainty estimates into the representation, the LLM can make more informed decisions in situations where object relationships are uncertain or evolving.

What are the potential limitations of the current approach in handling highly dynamic or unpredictable environments?

While the current approach of using the LLM-State representation for task planning in open-world environments offers significant advantages, there are potential limitations when dealing with highly dynamic or unpredictable environments:

Limited Adaptability: The current approach may struggle to adapt to rapidly changing environments where object attributes and relationships evolve quickly. In such scenarios, the LLM-State representation may not be able to capture real-time changes effectively, leading to suboptimal decision-making.

Complex Interactions: Highly dynamic environments with complex object interactions may pose challenges for the LLM-State representation. The representation may struggle to model intricate relationships between objects or predict the outcomes of complex interactions accurately.

Uncertain Environments: In unpredictable environments where object states are uncertain or ambiguous, the LLM-State representation may face difficulties in reasoning about object attributes and relationships. Handling uncertainty and variability in object interactions can be a significant challenge for the current approach.

Scalability: As the complexity and dynamics of the environment increase, the scalability of the LLM-State representation may become a limiting factor. Managing a large number of objects, attributes, and interactions in highly dynamic environments could strain the capacity of the representation.

How could the LLM-State representation be integrated with other robotic perception and control modules to enable more autonomous and adaptive task planning in the real world?

Integrating the LLM-State representation with other robotic perception and control modules can enhance autonomous and adaptive task planning in the real world by leveraging complementary capabilities. Here are some ways to integrate the LLM-State representation with other modules:

Perception Integration: Connect the LLM-State representation with perception modules to receive real-time updates on object detections, spatial information, and environmental changes. By integrating perception data, the LLM-State can adapt to dynamic environments and make informed decisions based on up-to-date information.

Control Module Interaction: Establish bidirectional communication between the LLM-State representation and the control module to enable seamless coordination between planning and execution. The LLM-State can provide action plans to the control module, receive feedback on action outcomes, and adjust plans based on real-world feedback.

Feedback Loop: Implement a feedback loop between the LLM-State representation and the perception-control pipeline to continuously update and refine the representation based on new observations and execution results. This feedback loop enables the system to learn from experience and improve task planning over time.

Adaptive Planning: Integrate adaptive planning mechanisms that allow the LLM-State representation to dynamically adjust plans based on changing environmental conditions, unexpected events, or failures during execution. By incorporating adaptive planning strategies, the system can respond flexibly to uncertainties and variations in the environment.

By integrating the LLM-State representation with perception and control modules in a cohesive manner, robotic systems can achieve greater autonomy, adaptability, and robustness in task planning and execution in real-world scenarios.