Differentiable Multi-Agent Navigation Policy via Kernel Velocity Fields
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Solution Overview
Problem
Existing multi-agent navigation technologies face challenges in developing efficient and collision-free navigation policies, particularly due to ill-defined gradient information and high offline training costs, which hinder the scalability and robustness of navigation systems.
Innovation Solution
The method involves a differentiable learning approach using a neural network to generate kernel-based divergence-free velocity fields, allowing for the interpolation of predicted velocities and iterative position updates of multiple objects, thereby constructing a scalable and efficient navigation policy that avoids collisions and reduces training costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional navigation policies are used, then agents can navigate in an environment, but the gradient information is ill-defined and training costs are high
Solution Approach 1:
The patent replaces traditional mechanical navigation systems with a differentiable neural network-based system. The neural network learns navigation policies through differentiable rendering, substituting the need for complex offline training with gradient-based optimization that provides well-defined gradient information and reduces training time.
Solution Approach 2:
The patent changes the parameters of the navigation system by using a differentiable neural network with learnable parameters that can be optimized through gradient descent. This allows the system to learn efficient navigation policies with well-defined gradients, overcoming the ill-defined gradient problem in traditional methods.
2Adaptability or versatility
If traditional navigation policies are used, then agents can navigate, but scalability and robustness are hindered by high offline training costs
Solution Approach 1:
The patent performs preliminary action by pre-computing a library of pre-trained neural network models that can be quickly deployed for different navigation scenarios. This allows the system to achieve scalability without requiring expensive offline training for each new scenario, as the pre-trained models can be adapted through fine-tuning.
Solution Approach 2:
The patent uses copying by creating a library of pre-trained neural network models that can be replicated and deployed for different navigation tasks. Instead of training from scratch for each scenario, the system copies and adapts pre-trained models, significantly reducing offline training costs and enabling scalability.
3Productivity
If a single policy is deployed for all agents, then inference efficiency improves, but the policy must handle diverse agent configurations
Solution Approach 1:
The patent applies universality by designing a single neural network policy that can handle multiple agent configurations and navigation scenarios. The differentiable neural network is trained to be universal, capable of adapting to different numbers of agents, initial positions, and target configurations through a single unified model, thereby improving inference efficiency without requiring separate policies for each scenario.
Data Source
AI summary
Methods, apparatus, and computer readable storage medium for navigating multiple objects from initial positions towards target positions are described in the present disclosure. The method includes obtaining an initial configuration and a target configuration, the initial configuration comprising initial positions of multiple objects, and the target configuration comprising target positions of the multiple objects; inputting the initial configuration and the target configuration into a neural network to generate a set of kernel parameters; constructing a kernel-based divergence-free velocity field based on the set of kernel parameters; interpolating the kernel-based divergence-free velocity field to extract predicted velocities of the multiple objects; generating next positions of the multiple objects based on the predicted velocities according to a differentiable navigation algorithm; and iteratively taking, until a condition is satisfied, next configuration as the initial configuration and feeding the next configuration into the neural network to begin next iteration.