LLM Robot Control Using Work Primitives for Autonomous Task Planning
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Solution Overview
Problem
Current robot control systems lack the ability to autonomously understand and execute complex tasks in dynamic environments without extensive human intervention, as they rely on predefined instructions and lack the capability to adapt to new objectives or environments effectively.
Innovation Solution
The integration of a large language model (LLM) into robot systems that captures sensor data, generates natural language descriptions of the environment, and interacts with an LLM module to receive task plans and work objectives, allowing the robot to autonomously execute tasks and adapt to new situations by breaking down objectives into reusable work primitives and generating task plans in natural language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robot control systems use predefined instructions, then they can execute basic tasks reliably, but they cannot adapt to new objectives or complex dynamic environments
Solution Approach 1:
The patent introduces a large language model as an intermediary between natural language instructions and robot control systems. The LLM translates human language into structured task plans and work objectives, enabling the robot to understand and execute complex tasks without requiring complex control system modifications. This mediator approach resolves the contradiction by handling adaptability at the language understanding level rather than at the control system level.
Solution Approach 2:
The patent segments complex tasks into hierarchical components: work objectives, task plans, and work primitives. This segmentation allows the robot to break down complex adaptive tasks into manageable, executable steps. By dividing the control process into discrete, structured components, the system achieves high adaptability without proportionally increasing overall system complexity.
2Extent of automation
If robot systems execute complex tasks autonomously, then human intervention is minimized, but the system requires advanced understanding and planning capabilities
Solution Approach 1:
The large language model serves as an intermediary that handles the cognitively demanding aspects of task understanding and planning. By offloading these complex processing requirements to the LLM, the robot system can achieve high autonomous execution capability without requiring the entire robot system to possess advanced understanding capabilities. The LLM acts as the brain while the robot executes the planned actions.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based control systems with an AI-based language model for high-level decision making and task planning. This substitution enables autonomous execution of complex tasks by using natural language processing and generation instead of complex programmed logic, thereby achieving automation without proportionally increasing mechanical or electronic system complexity.
3Adaptability or versatility
If robots understand natural language instructions, then they can perform diverse tasks, but they lack the capability to break down objectives into executable steps
Solution Approach 1:
The patent implements a hierarchical segmentation of tasks into work objectives, task plans, and work primitives. The large language model generates structured outputs that automatically decompose natural language instructions into executable components. This segmentation approach enables the robot to handle diverse tasks by breaking them down into standardized, executable steps without requiring complex planning algorithms.
Solution Approach 2:
The patent employs dynamic task planning where the large language model generates adaptive task plans based on the specific work objective and environment. The system can dynamically adjust the decomposition of tasks into work primitives based on the complexity and requirements of each specific task, allowing versatile task performance with a unified planning framework rather than multiple fixed planning systems.
Data Source
AI summary
Robot control systems, methods, control modules and computer program products that leverage one or more large language model(s) (LLMs) in order to achieve at least some degree of autonomy are described. Robot control parameters, environment details, and/or instructions may advantageously be specified in natural language (NL) and communicated with the LLM via an NL prompt or query. The NL query may include a request for one or more work objectives from the LLM, such as “What can I do here?”, thereby establishing a form of agency by which the robot system may identify activities to perform without operator intervention. The LLM may also be queried to convert each work objective into a task plan providing a sequence of steps that the robot system may execute to complete the work objective. Optionally, the robot system may communicate with an operator to determine whether or not to execute a task plan.