Autoencoder Latent Training Without Decoders for Real-Time Robotics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional variational autoencoders are computationally intractable for real-time or near-real-time robotics applications, failing to capture local features and being susceptible to background distractions, and they require reconstruction training that leads to overfitting.
Innovation Solution
Training an autoencoder using image pairs of a working environment at different time steps, focusing on latent representation prediction and reward prediction, without decoder stacks, to enhance feature extraction and robustness to noise, and integrating with reinforcement learning for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional variational autoencoders are used for feature extraction, then latent representations can be generated, but the computational complexity becomes intractable for real-time applications
Solution Approach 1:
The patent extracts only the essential encoder component from the traditional autoencoder architecture, removing the decoder portion. This extraction allows the model to focus computational resources on generating accurate latent representations without the overhead of reconstruction operations, thereby achieving real-time processing speeds while maintaining feature extraction accuracy.
Solution Approach 2:
The patent employs a simplified autoencoder architecture that is computationally lighter and more efficient for single-use inference tasks. By designing a model that does not require the full bidirectional transformation capability of traditional autoencoders, the system achieves faster processing suitable for real-time robotic applications where repeated training is not necessary.
2Manufacturing precision
If conventional autoencoders are trained using reconstruction loss, then the model can learn to reconstruct images, but the model becomes susceptible to background distractions and fails to capture local features
Solution Approach 1:
The patent introduces reward prediction as a feedback mechanism during training. Instead of relying solely on reconstruction loss, the model receives feedback through reward signals that guide it to focus on task-relevant local features and environmental changes. This feedback-driven approach enables the model to ignore background distractions while maintaining the ability to capture meaningful features for robotic manipulation.
3Manufacturing precision
If the autoencoder includes decoder stacks for reconstruction training, then the model can reconstruct input images, but the computational cost increases and training time extends
Solution Approach 1:
The patent extracts only the necessary encoder functionality from the complete autoencoder architecture, removing the decoder component entirely. This extraction eliminates the computational overhead associated with reconstruction operations during both training and inference, significantly reducing training time while preserving the core capability of generating accurate latent representations for feature extraction.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an autoencoder. One of the methods includes receiving a pair of images, wherein a first image of the pair represents a first state of a working environment at a first time step earlier than that of a second image of the pair. A first and a second latent representation is generated respectively for the first and the second image by the autoencoder; A predicted reward for an action executed in the first state is generated by a reward prediction neural network. A predicted next latent representation for the first state and the action is generated by a dynamics prediction neural network. An overall loss is determined based on the predicted reward, the predicted next latent representation, and the second latent representation. Model parameters of the autoencoder are updated to reduce the overall loss.