LLM Robot Planning With State Updates in Partial Observability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Large Language Models (LLMs) struggle to perform interactive planning in partially observable environments, where robots need to collect missing information and update state estimates to complete tasks, due to challenges in long-horizon chain-of-thought reasoning and data collection.
Innovation Solution
A framework using two LLMs - an LLM planner and an LLM evaluator - to decompose the task, where the planner generates action sequences based on task descriptions and current observations, and the evaluator assesses missing information and updates task states, enabling interactive planning in partially observable environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used for open-loop planning with perfect environment description, then task completion is achieved, but the system cannot handle partially observable environments with incomplete information
Solution Approach 1:
The patent implements a closed-loop planning system where the LLM continuously receives feedback from environment observations and updates its state estimates. The system observes environmental states, feeds this information back to the LLM, and adjusts planning accordingly, enabling reliable task completion in partially observable environments through iterative refinement of state estimates.
Solution Approach 2:
The patent performs preliminary state estimation and information gathering before final action execution. The LLM proactively identifies missing information, plans information-gathering actions, and pre-estimates states before committing to final task actions, improving adaptability to partial observability.
2Measurement precision
If LLMs perform chain-of-thought reasoning in partially observable environments, then better state estimation is achieved, but computational complexity and reasoning time increase
Solution Approach 1:
The patent segments the complex reasoning process into distinct modular components: observation processing, state estimation, missing information identification, and action planning. Each module handles a specific aspect of the reasoning task, reducing overall complexity while maintaining state estimation accuracy through specialized processing at each stage.
Solution Approach 2:
The patent applies partial reasoning by focusing computational effort only on the most critical aspects of state estimation and missing information identification. Rather than performing exhaustive reasoning on all possible factors, the system selectively reasons about relevant state variables and information gaps, reducing complexity while preserving essential accuracy.
3Loss of information
If LLMs collect missing information from environment, then task planning accuracy improves, but time and computational resources are consumed
Solution Approach 1:
The patent performs preliminary identification of missing information through state estimation before actual information collection. By anticipating what information is needed and planning information-gathering actions in advance, the system reduces redundant observations and optimizes the timing of data collection, balancing information completeness with time efficiency.
Solution Approach 2:
The patent collects only the essential missing information required for accurate state estimation and task planning, rather than gathering all possible environmental data. This selective information collection approach reduces time and computational resource consumption while maintaining sufficient information completeness for effective planning.
Data Source
AI summary
A controller is provided for manipulating objects by a robot arm having a gripper. The controller includes a large language model (LLM) planner configured to acquire the states and the task description and generate an action sequence command that operates the robot arm with the gripper based on the task description, the current observations, historical information including historical actions and historical observations from previous steps. The controller includes a robot interface configured to generate and transmit commands to operate the robot arm in response to the action sequence from the LLM planner and an LLM evaluator configured to acquire the states, the current observations, analyze the historical information including historical actions and historical observations from previous steps, evaluate missing information required to complete the task, evaluate new information acquired by the sensors, estimate a task-execution status after the robot arm operated the generated action sequence, and output updated information to the LLM planner.