Robot Control Using LLM Behavior Prediction in Dynamic Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robot control methods struggle to safely and effectively navigate dynamic environments where humans and other obstacles are present, as they fail to accurately predict and respond to the behavior of agents in these environments.
Innovation Solution
A method involving the collection of state information about agents in the environment, conversion of this information into textual descriptions, and the use of large language models to predict agent behavior and generate task plans for the robot, allowing it to safely operate in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional robot control methods are used, then the robot can operate in structured environments, but it fails to safely navigate dynamic environments with humans and obstacles
Solution Approach 1:
The patent introduces large language models as an intermediary between sensor data and robot control decisions. The LLM processes textual state descriptions of the environment and generates predictions about agent behavior, serving as a mediator that bridges traditional perception systems and control systems, enabling safe operation in dynamic environments
Solution Approach 2:
The patent replaces traditional mechanical control algorithms with language model-based predictive systems. Instead of using conventional motion planning and collision avoidance algorithms, the system uses LLMs to predict future states and generate task plans, substituting mechanical control approaches with cognitive-like processing
2Measurement precision
If complex control algorithms are used to predict agent behavior, then navigation accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent uses large language models trained on extensive textual data about human behavior and environments. The LLMs copy patterns and knowledge from training data to make predictions about agent behavior, allowing the system to achieve high prediction accuracy without implementing complex real-time simulation or reasoning algorithms
Solution Approach 2:
The patent performs preliminary training of large language models offline using extensive datasets. This preliminary action transfers knowledge to the model before deployment, enabling the robot to make accurate predictions during operation without requiring complex real-time computations, thus reducing operational computational complexity
3Speed
If real-time processing of sensor data is implemented, then responsive control is achieved, but data processing completeness and accuracy decrease
Solution Approach 1:
The patent introduces textual state descriptions as an intermediary representation that captures essential environmental information in a compact format. The LLM processes these textual descriptions rather than raw sensor data, maintaining information completeness while enabling faster processing through natural language understanding
Solution Approach 2:
The patent transforms sensor data into textual parameters that describe the state of the environment and agents. This parameter transformation changes the data representation from continuous sensor readings to discrete textual descriptions, enabling efficient processing while preserving critical information about the environment
Data Source
AI summary
A method for controlling a robot apparatus. The method includes collecting state information about an agent located in an environment of the robot apparatus, converting the state information about the agent into a textual state description, feeding the textual state description to a large language model for generating a prediction of a behavior of the agent, a future state of the agent and/or the environment, generating a task plan for the robot apparatus taking into account the prediction, and controlling the robot apparatus according to the task plan.