Robot Insertion Control Using Camera-Labeled Movement Vectors
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Solution Overview
Problem
Existing robot control methods for insertion tasks, such as peg-in-hole, are inefficient and slow, particularly when dealing with complex shapes and variations in location, and existing visual techniques are three times slower than human operators.
Innovation Solution
A method for training a neural network to derive a movement vector from a robot-mounted camera image, involving controlling the robot to move away from a target position, taking images, and labeling them with movement vectors to train the network, using data augmentation and force measurements to improve generalization and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If existing visual techniques are used for robot insertion tasks, then the robot can perform insertion operations, but the operation speed is three times slower than human operators
Solution Approach 1:
The patent replaces traditional mechanical vision-based control systems with a neural network-based system that processes camera images to directly generate movement vectors. This substitution enables the robot to achieve insertion speeds comparable to human operators by leveraging the neural network's ability to rapidly process visual information and generate precise motion commands without the computational overhead of traditional visual techniques
Solution Approach 2:
The patent transforms the control approach by changing from position-based control to movement vector-based control. The neural network outputs movement vectors that directly specify the direction and magnitude of corrections needed, allowing the robot to make rapid, intuitive adjustments during insertion tasks, thereby significantly improving operation speed and efficiency
2Adaptability or versatility
If traditional robot control methods are used for complex insertion tasks with variations in location and shape, then the robot can handle simple cases, but it fails to generalize to complex variations
Solution Approach 1:
The patent performs preliminary training of the neural network using a dataset of camera images and corresponding movement vectors collected from various insertion scenarios. This offline training phase enables the network to learn general patterns and adapt to different object shapes, locations, and orientations before actual insertion tasks, ensuring high reliability when encountering variations not explicitly seen during training
Solution Approach 2:
The patent uses camera images to create a visual representation of the insertion scenario, which the neural network processes to generate movement vectors. This copying of the visual scene into the neural network's processing space allows the system to generalize across different physical configurations without requiring physical reprogramming, thereby improving adaptability to variations in location, shape, and orientation
3Loss of information
If training data is collected from real robot operations, then the neural network can be trained, but safety risks and operational disruptions occur
Solution Approach 1:
The patent introduces an intermediary data collection process where training data is gathered through simulated or controlled movements rather than through actual insertion operations. The neural network is trained on this intermediary dataset, which captures the essential visual-motor relationships without requiring the robot to perform potentially harmful real-world operations, thereby eliminating safety risks while maintaining training data quality
Solution Approach 2:
The patent performs preliminary data collection and neural network training in advance, before deploying the system for actual insertion tasks. This preliminary training phase allows the collection of comprehensive training data under controlled conditions without safety concerns, and the trained network is then deployed for production operations, separating the data collection phase from the operational phase to eliminate safety risks
Data Source
AI summary
A method for training a neural network to derive, from an image of a camera mounted on a robot, a movement vector for the robot to insert an object into an insertion. The method includes controlling the robot to hold the object, bringing the robot into a target position in which the object is inserted in the insertion, for a plurality of positions different from the target position controlling the robot to move away from the target position to the position, taking a camera image by the camera and labelling the camera image by a movement vector to move back from the position to the target position and training the neural network using the labelled camera images.