Neural Network Training for Robot Control with Pose Uncertainty
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Solution Overview
Problem
Existing robot control systems struggle to generate precise control parameters for manipulating objects in varying poses, as they fail to account for uncertainties in camera and object calibration, leading to inefficiencies in tasks like gripping and manipulation.
Innovation Solution
A method for training a neural network that simulates camera images with random variations in camera and object poses, using uncertainty areas to generate robust training data, allowing the network to predict precise robot control parameters for handling objects in different orientations and positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robot control systems use fixed camera pose and object pose for training, then training data generation is simple, but the system fails to account for uncertainties in camera and object calibration, reducing reliability
Solution Approach 1:
The patent applies preliminary action by pre-generating training data with random variations in camera pose and object pose before actual robot operation. This prepares the neural network in advance for calibration uncertainties, allowing it to robustly handle varying conditions during deployment without requiring complex real-time adjustments.
Solution Approach 2:
The patent implements parameter changes by systematically varying camera pose parameters (position and orientation) and object pose parameters within defined uncertainty ranges during training data generation. This exposes the neural network to a broad range of possible calibration variations, enhancing its reliability when deployed with uncertain calibration parameters.
2Adaptability or versatility
If robot control systems use random variations in training data, then robustness to calibration uncertainties improves, but training data generation becomes more complex
Solution Approach 1:
The patent applies dynamics by introducing dynamic random variations in camera pose and object pose parameters during training data generation. Instead of using fixed parameters, the system dynamically samples from probability distributions representing calibration uncertainties, enabling the neural network to adapt to varying conditions while maintaining a structured training approach.
Solution Approach 2:
The patent uses preliminary action to pre-generate diverse training data covering the full range of expected pose variations before deployment. This preliminary exposure to varied conditions builds the neural network's adaptability in advance, reducing the need for complex adaptive mechanisms during actual operation.
3Manufacturing precision
If precise control parameters are generated for each object pose, then manipulation accuracy improves, but the system requires extensive training data covering all possible poses
Solution Approach 1:
The patent implements parameter changes by systematically varying camera pose parameters (x, y, z position and roll, pitch, yaw orientation) and object pose parameters within defined uncertainty ranges. This generates diverse training examples from a compact parameter space, enabling precise control parameter generation without requiring exhaustive training data for every possible pose configuration.
Solution Approach 2:
The patent applies preliminary action by pre-generating training data that covers the full range of expected pose variations using parameter sampling. This preliminary comprehensive coverage allows the neural network to learn precise control parameter mappings for various poses from a manageable training set, avoiding the need for exhaustive real-world data collection.
Data Source
AI summary
A method for training a neural network for controlling a robot. The method includes ascertaining a camera pose in a robot cell and an uncertainty area around the ascertained camera pose, ascertaining an object area in the robot cell, generating training-camera-images, for each training-camera-image a training-camera-image camera pose being randomly established in the uncertainty area around the ascertained camera pose, a training-camera-image object pose being randomly established in the object area and the training-camera-image being generated, so that it shows the object with the training-camera-image object pose from the perspective of a camera with the training-camera-image camera pose, generating training data from the training-camera-images, each training-camera-image being assigned one or multiple training-robot control parameters for handling the object in the training-camera-image object pose of the training-camera-image, and training the neural network using the training data.