Robot Controller Training for Autonomous Obstacle Navigation
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Solution Overview
Problem
Current methods for controlling robot devices require constant human intervention, as users must provide precise inputs to navigate around obstacles, which is taxing and inefficient, especially in complex environments.
Innovation Solution
A neural network-based method is developed, comprising an encoder, decoder, and policy network, where the encoder encodes images into features, the decoder determines traversability and distance, and the policy network generates control information for autonomous robot movement, allowing for high-level commands like 'go forward' or 'turn left' without prior knowledge of the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If remote control by human user is used, then robot can be controlled to navigate around obstacles, but constant human attention and precise inputs are required
Solution Approach 1:
The robot performs self-navigation by processing visual data through the neural network (encoder, decoder, and policy networks) to autonomously determine movement commands, eliminating the need for continuous human intervention while maintaining reliable obstacle avoidance and path following
2Extent of automation
If autonomous navigation is implemented, then human intervention is reduced, but the system requires complex neural network processing
Solution Approach 1:
The control system is segmented into distinct functional networks: encoder network for feature extraction, decoder network for spatial understanding, and policy networks for decision-making. This modular architecture manages complexity by assigning specific tasks to each network component while working together for autonomous navigation
Solution Approach 2:
The encoder-decoder networks serve as intermediaries that transform raw visual data into meaningful features and spatial representations, which then guide the policy networks in generating movement commands, creating a structured information flow that manages system complexity
3Ease of operation
If high-level commands are used, then user input is simplified, but the robot needs to interpret and execute commands autonomously
Solution Approach 1:
The policy networks transform high-level semantic commands into low-level motor control parameters by learning the mapping between command types and movement actions through training, enabling the robot to interpret various command formats and execute appropriate navigation behaviors
Data Source
AI summary
A method for training a robot device controller is described comprising training a neural network comprising an encoder network, a decoder network and a policy network, such that, for each of a plurality of digital training input images, the encoder network encodes the digital training input image to a feature in a latent space, the decoder network determines, from the feature, for each of a plurality of areas shown in the digital training input image, whether the area is traversable and information about the distance between the viewpoint of the digital training input image and the area and the policy model determines, from the feature, control information for controlling movement of a robot device wherein at least the policy model is trained in a supervised manner using control information ground truth data of the digital training input images.