Robot Navigation Neural Adapters for Multi-Task Path Optimization
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Solution Overview
Problem
Existing robot navigation systems face challenges in efficiently optimizing trajectories for multiple tasks due to the computational complexity of combinatorial optimization problems, requiring problem-specific heuristic algorithms that do not scale well to real-world applications and introduce additional computational expense.
Innovation Solution
A single deep neural network architecture with adapter layers is used to learn heuristics for various navigation tasks, allowing large parameter sharing across multiple combinatorial optimization problems, including a backbone neural network and task-specific adapters for node, edge, and output representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate neural models are trained for each navigation task, then task-specific performance is improved, but training time and computational resources increase significantly
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 modules that can be rapidly configured for different problems, eliminating the need to train separate models for each task while maintaining competitive performance across all task types.
Solution Approach 2:
The neural network architecture is segmented into a shared backbone network and task-specific adapter components. The backbone network learns general combinatorial optimization patterns, while lightweight adapters are configured for specific tasks. This segmentation allows the system to benefit from both generalization across tasks and specialization for individual problems, significantly reducing overall training time.
2Manufacturing precision
If problem-specific heuristic algorithms are used, then solution quality is improved, but adaptability to new problem types deteriorates
Solution Approach 1:
The patent develops a universal neural network framework that can solve multiple combinatorial optimization problems using a single model architecture. The network processes different problem types (routing, scheduling, graph problems) through unified attention mechanisms and can be adapted to new problem types without retraining the entire model, thus maintaining both solution quality and adaptability.
Solution Approach 2:
The system dynamically configures task-specific adapter modules based on the input problem type. Rather than using fixed problem-specific algorithms, the network adapts its processing approach dynamically according to the characteristics of the current optimization problem, enabling both high solution quality and flexibility across diverse task types.
3Reliability
If traditional exact solvers or approximation algorithms are used, then optimality guarantees are improved, but computational scalability deteriorates
Solution Approach 1:
The patent replaces traditional mechanical optimization algorithms (exact solvers and approximation algorithms) with a neural network-based system. The network learns optimization strategies through training and can rapidly generate high-quality solutions for large-scale problems, achieving both good solution quality and computational scalability that traditional algorithms cannot provide.
Solution Approach 2:
The system changes the fundamental approach from algorithmic parameter tuning to learned parameter optimization. Instead of using fixed algorithmic parameters that require manual tuning for each problem size, the neural network learns optimal solution patterns from training data, enabling it to scale efficiently to larger problem instances while maintaining solution quality.
Data Source
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AI summary
A method for robot navigation is disclosed, the method comprising receiving a backbone neural network, the backbone neural network having been trained to solve a set of robot navigation tasks comprising a first robot navigation task of optimizing a first path visiting a first number of first locations when considering first path costs. The method further comprises receiving a second robot navigation task, wherein the second robot navigation task comprises optimizing a second path visiting a second number of second locations when considering second path costs. The method further comprises configuring adapter layers for the backbone neural network for forming a neural network pipeline, wherein the adapter layers comprise a node adapter, an edge adapter and an output adapter. The method further comprises feeding the second locations and the second path costs to the neural network pipeline to determine a path for an autonomous machine based on the output.