Human-in-the-Loop Robot Training Using MR Feedback
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Solution Overview
Problem
Current methods for training and testing robot control systems using generative AI face challenges in obtaining sufficient data efficiently, as they rely heavily on low-level libraries, making it difficult to identify and improve failure causes in complex robot actions, especially when multiple steps fail, leading to an ineffective, time-consuming, and costly process.
Innovation Solution
A human-in-the-loop method is introduced, where high-level instructions from AI models are converted into human-operated robot tasks, deployed on a Mixed-Reality (MR) system, allowing a human data collector to execute tasks and provide feedback, which is used to improve the low-level libraries and algorithms, enabling real-time identification of failure causes and multiple-step corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If high-level instructions from LLM are deployed directly on a robot, then the robot can execute autonomous tasks, but the system fails when low-level libraries are incapable of a task and there is no clear way to improve performance
Solution Approach 1:
The patent introduces a human-in-the-loop intermediary between the high-level LLM instructions and the robot execution. The human operator monitors task execution, identifies failures, and provides feedback to improve low-level libraries. This mediator resolves the contradiction by maintaining automation while adding reliability through human oversight and iterative improvement.
Solution Approach 2:
The system implements a feedback mechanism where human operators observe robot task execution, identify failures in low-level libraries, and use this information to improve the libraries. This closed-loop feedback process enhances system reliability while preserving autonomous execution capabilities.
2Measurement precision
If a user manually monitors and parses log files to identify failure causes, then the user can detect problems, but the process is ineffective, time-consuming, and expensive especially when multiple steps fail
Solution Approach 1:
The human operator serves as an intermediary who systematically monitors task execution and log files, providing more efficient failure detection than automated systems alone. The human's ability to understand context and prioritize issues reduces time loss while maintaining accurate failure identification.
Solution Approach 2:
The system structures log file generation and task breakdown in advance, preparing information in a format that enables efficient human analysis. By organizing logs and task steps beforehand, the human operator can quickly identify failure causes without manual parsing of unstructured data.
3Quantity of substance
If extensive robot testing is conducted to train machine learning models, then sufficient training data is obtained, but the process is costly and time-consuming
Solution Approach 1:
The system uses simulation environments and virtual copies of the robot to generate training data instead of extensive physical robot testing. This allows sufficient training data to be obtained without the time and cost penalties of real-world robot experimentation.
Solution Approach 2:
The system performs preliminary data collection and model training in simulation environments before deploying to physical robots. This preliminary action reduces the need for extensive time-consuming field testing while still obtaining sufficient training data.
Data Source
AI summary
A robot teaching and testing system and method that performs human-operated robot tasks according to instructions generated from generative AI models. The process starts with a user prompt and combines the user prompt with predefined prompt templates to generate well-formatted text prompts. Generative AI models take the text prompts and convert them into high-level instructions or control codes that can be deployed on a robot. The high-level instructions are then converted into human-operated robot tasks for a human data collector using a mixed reality (MR) device. The human data collector will attempt to follow the instructions to complete the human-operated robot tasks and may overwrite the suggested instructions by performing a different action, demonstrate a task without instructions, or leave feedback or comments regarding the tasks. Feedback data will be captured and saved for improving the robot system.