Assembly Robot Control With LLM Skill Switching for Precision Tasks
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Solution Overview
Problem
Existing generalist robotic policies struggle to achieve high-precision tasks in industrial assembly due to the need for intricate factors like contact engagement, friction handling, and refined motor skills, which require additional sensory data for enhanced precision.
Innovation Solution
A globally robust control policy using Large Language Models (LLMs) with dynamic context switching, integrating specialized skills models for precise robotic operations, and a centralized controller to predict and execute a sequence of skills based on natural language prompts and sensor inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generalist policy is used to control robots in various environments, then adaptability is improved, but manufacturing precision deteriorates because generalist policies cannot handle intricate factors like contact engagement and friction with sufficient accuracy
Solution Approach 1:
The control system is segmented into a generalist policy module that handles high-level decision-making and environment understanding, and specialized skill modules that handle specific precision tasks like insertion. The generalist policy selects which specialized skill to invoke based on the current situation, allowing the system to maintain both broad adaptability and task-specific precision.
Solution Approach 2:
The generalist policy acts as an intermediary between the environment and specialized skill modules. It processes sensor inputs and language instructions to determine the appropriate skill to execute, bridging the gap between general environment understanding and specific task execution without requiring each skill module to handle all types of inputs.
2Manufacturing precision
If specialized skill models are integrated for high-precision tasks, then manufacturing precision is improved, but device complexity increases due to the need for multiple independent skill models and dynamic context switching
Solution Approach 1:
The generalist policy serves multiple functions: it understands natural language instructions, processes visual inputs from various sensors, selects appropriate specialized skills, and manages the overall control flow. This multi-functional component reduces the need for separate control logic in each skill module, thereby managing complexity while enabling precision through specialization.
Solution Approach 2:
The system dynamically switches between different specialized skill models based on the current task requirements and environment state. The generalist policy continuously monitors sensor inputs and language instructions to determine which skill should be active at any given moment, allowing the system to adapt its complexity level to match the task demands rather than maintaining maximum complexity for all situations.
3Manufacturing precision
If additional sensory data like force or torque measurements are integrated, then manufacturing precision is improved, but device complexity increases due to the need for further sensory integration and processing
Solution Approach 1:
The generalist policy is designed to handle multiple types of sensory inputs uniformly, including vision data and force/torque measurements. It processes these diverse inputs through a common framework to make decisions about which skill to execute, avoiding the need for separate processing pipelines for each sensor type and thereby managing complexity while enabling precision through multi-sensory integration.
Data Source
AI summary
A computer-implemented method of determining actions for controlling a robot, in particular an assembly robot, includes (i) receiving a first and second input, wherein the first input is a sentence describing an action which should be carried out by the robot, wherein the second input is an image of a current state of an environment of the robot, (ii) feeding the first input into a first machine learning model and feeding the second input into a second machine learning model, wherein the first and second machine learning models are configured to determine tokens for their respective inputs, and (iv) feeding the tokens into a third machine learning model, wherein the third machine learning model outputs two outputs, wherein the first output is a switch for incorporating specialized skill networks and the second output are actions.