Robotic Task Prediction Using Deep Learning and Model-Based Control
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Solution Overview
Problem
Current neural network-based control systems for robotic tasks are limited by the need for extensive human-curated data sets and simulations, which can be incomplete or unrealistic, leading to suboptimal performance, especially in real-world scenarios, with success rates often not exceeding 87% compared to human-devised controllers.
Innovation Solution
A computing system that trains machine learning models to predict robotic tasks by monitoring model-based control algorithms, allowing these models to learn from real-world data and improve over time, providing predictions to the control algorithms to enhance task performance and adapt to variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks are trained using supervised training with hand-annotated data sets, then the neural network can learn from examples, but the training process requires many man-years to create complete data sets and may not contain the right kind of information for the deep learning task
Solution Approach 1:
The patent uses simulation environments to create artificial representations of real-world tasks, copying the essential features of physical scenarios without requiring physical prototypes or extensive manual data collection. This allows training data to be generated automatically through simulated robot interactions with virtual objects and environments.
Solution Approach 2:
The system employs self-supervised learning where the neural network learns from its own experiences and interactions within the simulation environment. The robot autonomously explores the simulated world, discovers patterns, and improves its task performance without continuous human intervention or manual annotation of training data.
2Extent of automation
If neural networks are trained by allowing them to operate on their own and detect errors, then learning can occur automatically, but errors may result in damage to process equipment or product in real-life situations
Solution Approach 1:
The patent implements a simulation buffer between the neural network's learning process and the physical robot system. The simulation environment absorbs potential errors and failures during training, preventing them from reaching the physical system. Only after successful training in simulation does the learned behavior transfer to the physical robot, eliminating the risk of damage during the learning phase.
Solution Approach 2:
The system creates a virtual copy of the physical robot and its environment for training purposes. This digital twin allows the neural network to learn through trial and error without any risk to physical equipment or products, while still preparing the system for real-world deployment.
3Loss of time
If simulations are used to train neural networks, then time and damage limitations of real-life training are overcome, but the representation of the task and environment is necessarily simplified and may remove critical data
Solution Approach 1:
The patent employs dynamic simulation environments that can adapt and evolve during the training process. The simulation complexity and fidelity are adjusted based on the learning stage and task requirements, allowing the system to start with simplified models and progressively incorporate more detailed and realistic features as the neural network develops competence.
Solution Approach 2:
The system varies simulation parameters such as object properties, environmental conditions, and task constraints to ensure comprehensive training coverage. By systematically changing these parameters, the neural network learns to handle diverse scenarios and maintains robustness without requiring an excessively complex static simulation model.
Data Source
AI summary
A computing system is provided for training one or more machine learning models to perform at least a portion of a robotic task of a physical robotic system by monitoring a model-based control algorithm associated with the physical robotic system perform at least a portion of the robotic task. One or more robotic task predictions may be defined, via the one or more machine learning models, based upon, at least in part, the training of the one or more machine learning models. The one or more robotic task predictions may be provided to the model-based control algorithm associated with the physical robotic system. The robotic task may be performed, via the model-based control algorithm associated with the robotic system, on the physical robotic system based upon, at least in part, the one or more robotic task predictions defined by the one or more machine learning models.