Neural Navigation Pipeline With Adapters for Multi-Task Path Optimization
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Solution Overview
Problem
Existing robot navigation systems face challenges in efficiently optimizing trajectories for visiting multiple locations while considering diverse requirements such as payload, distance, and time constraints, often relying on problem-specific heuristics that do not scale well to real-world applications and introduce computational expense.
Innovation Solution
A single deep neural network architecture with adapter layers is used to learn heuristics for multiple navigation problems, allowing large parameter sharing across tasks, and employing a backbone neural network with node, edge, and output adapters to determine paths for autonomous machines, leveraging few-shot training and reinforcement/imitation learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If problem-specific heuristic algorithms are used for each combinatorial optimization task, then solution quality is improved, but device complexity and adaptability worsen due to needing separate models for each task type
Solution Approach 1:
The patent implements a universal backbone neural network that can handle multiple combinatorial optimization tasks (TSP, CVRP, VRPTW, OP, KP, MWVC, MCLP) through a single model. The network uses task-specific adapter layers that can be quickly configured for different problem types, eliminating the need for separate trained models for each task while maintaining competitive solution quality across all problem classes.
Solution Approach 2:
The model architecture is segmented into a shared backbone neural network and task-specific adapter layers. The backbone contains general-purpose computational blocks (attention mechanisms, feed-forward networks) that handle common optimization patterns, while adapter layers are selectively activated or configured based on the specific task type, allowing efficient task adaptation without retraining the entire model.
2Measurement precision
If separate neural models are trained for each navigation problem type, then task-specific performance is improved, but loss of time and productivity worsen due to multiple training processes
Solution Approach 1:
The patent merges multiple task-specific models into a single multi-task neural network framework. By training one unified model on diverse combinatorial optimization problems simultaneously, the system achieves comparable performance to task-specific models while eliminating the need for multiple separate training processes, significantly reducing total training time and computational resources.
Solution Approach 2:
The universal backbone network is trained to learn general optimization strategies that transfer across different problem types. Task-specific adaptations are achieved through lightweight adapter layers rather than full model retraining, allowing rapid adaptation to new task types with minimal additional training time.
3Measurement precision
If hybrid models with beam search and simulation-guided techniques are used, then solution quality is improved, but use of energy and device complexity worsen due to additional computational expense
Solution Approach 1:
The neural network model is designed to directly output high-quality solutions without requiring external beam search or simulation-guided optimization steps. The network architecture incorporates attention mechanisms and value-based selection that inherently perform the optimization function, making additional heuristic search steps unnecessary and reducing overall computational energy requirements.
Data Source
AI summary
A method for robot navigation includes: receiving a backbone neural network, the backbone neural network trained to solve a set of autonomous navigation tasks including a first autonomous navigation task, where the first autonomous navigation task includes optimizing a first path visiting a first number of first locations when considering first path costs; receiving a second autonomous navigation task, where the second autonomous navigation task includes optimizing a second path visiting a second number of second locations when considering second path costs, where the second locations are locations in an environment of an autonomous machine, where the second number is different from the first number; configuring adapter layers for the backbone neural network for forming a neural network pipeline; and feeding the second locations and the second path costs to the neural network pipeline and determining a path for the autonomous machine based on an output.