Interacting Gaussian Process Crowd Navigation for Intent Alignment
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Solution Overview
Problem
Classical congestion navigation algorithms fail to optimize intent or flexibility in congested environments, requiring handcrafted objective functions and large data sets, and struggle with multi-faceted intent and host-agent agreement.
Innovation Solution
A system utilizing interacting Gaussian processes, specifically zero free-parameter Gaussian processes (zpIGP), models host-agent interaction by calculating Gaussian processes for each agent and determining an objective function based on intent and flexibility, optimizing crowd navigation with a convex configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical congestion navigation algorithms use deep learning and deep reinforcement learning based approaches, then navigation optimization in congested environments is achieved, but the system fails to optimize intent or flexibility and requires handcrafted objective functions
Solution Approach 1:
The system uses Gaussian processes to automatically learn the objective function from data, eliminating the need for handcrafted objective functions. The statistical module independently determines the objective function based on observed agent behaviors and interactions, making the system self-configuring rather than requiring manual parameter tuning.
Solution Approach 2:
The patent transforms the rigid handcrafted objective functions into flexible Gaussian process models with learnable parameters. By changing from fixed parameters to probabilistic parameters that can be inferred from data, the system achieves both navigation optimization and intent/flexibility optimization simultaneously.
2Reliability
If classical congestion navigation algorithms use data driven representations, then large amounts of data are required, but the system fails to optimize intent or flexibility
Solution Approach 1:
The system pre-defines the probabilistic structure of the objective function using Gaussian processes before data collection. This preliminary statistical framework allows the system to learn from smaller data sets by incorporating prior knowledge about agent behaviors and interactions, reducing the need for large amounts of training data.
Solution Approach 2:
Gaussian processes serve as an intermediary between raw sensor data and the objective function. Instead of directly processing large amounts of data, the system uses the Gaussian process statistical model as a mediator that captures essential patterns and relationships, enabling intent and flexibility optimization with reduced data requirements.
3Adaptability or versatility
If classical congestion navigation algorithms are used, then the system may fail to handle multi-faceted intent correctly, but the patent utilizes interacting Gaussian processes to model host-agent interaction
Solution Approach 1:
The system segments the complex multi-faceted intent into separate Gaussian process models for different aspects of agent behavior. Each Gaussian process captures a specific dimension of intent (e.g., position, velocity, interaction preferences), allowing the system to handle multiple facets of intent independently and then combine them coherently.
Solution Approach 2:
The patent combines multiple Gaussian process models into a composite probabilistic framework that captures complex host-agent interactions. By composing simpler Gaussian process models into a unified interacting processes model, the system achieves versatile multi-faceted intent handling while maintaining mathematical tractability through the properties of Gaussian processes.
Data Source
AI summary
Systems and methods for utilizing interactive Gaussian processes for crowd navigation are provided. In one embodiment, a system for a crowd navigation of a host is provided. The system includes a processor, a statistical module, and a model module. The processor receives sensor data. The statistical module identifies a number of agents in a physical environment based on the sensor data. The statistical module further calculates a set of Gaussian processes. The set of Gaussian processes includes a Gaussian Process for each agent of the number of agents. The statistical module further determines an objective function based on an intent and a flexibility. The model module generates a model of the number of agents by applying the objective function to the set of Gaussian processes. The model includes a convex configuration of the number of agents in the physical environment.