Robot Arm Action Model for Language Control
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Solution Overview
Problem
Existing robot control systems face challenges in accurately and efficiently combining robot technology with machine learning to perform tasks due to insufficient training data and inefficiencies in controlling robot actions based on language descriptions and current states.
Innovation Solution
A method and apparatus that utilize a pre-trained and fine-tuned action model to determine robot arm actions based on language descriptions and current states, incorporating a pre-training process with character video data and fine-tuning with robot arm data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robot control systems use traditional methods to combine robot technology with machine learning, then the system structure remains simple, but the accuracy and efficiency of task execution are insufficient due to lack of training data
Solution Approach 1:
The patent applies preliminary action by pre-training the action model using character video data before fine-tuning it with robot arm data. This two-stage training approach prepares the model in advance with general motion understanding, enabling more accurate robot control with limited robot-specific training data, thus improving accuracy while managing system complexity
Solution Approach 2:
The patent introduces an action model as an intermediary component that bridges language descriptions, visual data, and robot arm control. This intermediary model processes and integrates multiple data types (language, video) to generate control actions, improving task execution accuracy while maintaining a modular system architecture that manages complexity
2Measurement precision
If robot control systems use insufficient training data, then the system remains simple to train, but the accuracy of determining robot actions based on language descriptions and current states deteriorates
Solution Approach 1:
The patent uses preliminary action by pre-training the model on large-scale character video data that captures diverse human motions and behaviors. This preliminary training accumulates general motion understanding that transfers to robot control tasks, enabling accurate action determination even when robot-specific training data is limited
Solution Approach 2:
The patent applies parameter changes by transitioning the model from processing human character data to processing robot arm data through fine-tuning. This parameter adaptation allows the model to leverage knowledge from abundant character video data while adjusting to robot-specific dynamics, improving action determination accuracy without requiring proportional increases in robot training data
3Productivity
If robot control systems use inefficient methods to process language descriptions and current states, then the processing speed remains fast, but the efficiency of controlling robot actions deteriorates
Solution Approach 1:
The patent merges language processing and visual data processing into a unified action model that simultaneously handles both inputs. This integrated approach processes language descriptions and current state visual data in parallel through the same model architecture, improving the efficiency of determining robot actions while reducing the time lost to sequential processing
Solution Approach 2:
The patent implements feedback by using the current state of the robot arm as input to the action model, which then determines the next action based on both the language description and the current state. This closed-loop feedback mechanism ensures that action determination is efficient and adaptive, improving overall productivity by continuously adjusting actions based on real-time state information
Data Source
AI summary
Methods, devices, and media for operating a robot arm are provided. In one method, receive a language description for specifying a target implemented by the robot arm; obtain a current state of the robot arm; and determine, according to an action model, an action to be performed by the robot arm based on the language description and the current state. With the example implementation of the present disclosure, the problem of insufficient training data of the robot arm may be alleviated. Further, the pre-trained action model may obtain the basic knowledge about the association relationship between the language description and the person action, may obtain a more accurate action model, and further obtain the action of the robot arm matching the language description in a more efficient manner.