Model Predictive Control With VAE for Multi-Agent Interaction Optimization
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Solution Overview
Problem
Existing control methodologies for agents like robots and vehicles fail to optimize interactions with their environment and other agents in a way that minimizes harm or resource consumption, such as ergonomic considerations and power usage, especially when switching between different optimization strategies based on terrain and task requirements.
Innovation Solution
A computer-implemented method using model predictive control (MPC) with variational autoencoders to analyze environment and dynamic data, outputting probabilistic action estimates for optimal control trajectories that consider global constraints and optimize interactions, ensuring robust and efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional control methodologies are used for agent operations, then the control system is simple to implement, but the agent cannot optimize interactions to minimize harm or resource consumption
Solution Approach 1:
The patent introduces a cost function as an intermediary element that mediates between the agent's actions and the environment. This cost function quantifies harm and resource consumption, allowing the optimization algorithm to minimize these factors without directly complexifying the control execution. The cost function acts as a bridge that translates complex optimization goals into manageable computational form.
Solution Approach 2:
The patent replaces traditional mechanical control systems with a data-driven optimization approach using neural networks and cost functions. Instead of hard-coded control rules, the system uses learned policies from training data and continuous optimization based on cost functions, substituting mechanical control logic with intelligent algorithms that can adaptively minimize harm and resource usage.
2Reliability
If model predictive control with variational autoencoders is implemented, then optimal control trajectories are achieved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training variational autoencoders on extensive environment data and interaction patterns before actual operation. The neural networks learn optimal control policies and cost function relationships during offline training, so that during real-time operation, the system can quickly infer optimal trajectories without performing heavy computational optimization from scratch, thus achieving robustness with manageable real-time complexity.
Solution Approach 2:
The patent uses variational autoencoders to create compressed representations (encodings) of complex environment states and trajectories. Instead of processing full high-dimensional state spaces, the system works with compressed latent representations that capture essential features, reducing computational complexity while maintaining the ability to generate optimal control trajectories through the decoder.
3Adaptability or versatility
If the agent adapts to changing environmental conditions, then interaction optimality is maintained, but the system requires continuous data processing and model updates
Solution Approach 1:
The patent implements periodic action by updating the cost function and re-running optimization only at necessary intervals or when significant environmental changes are detected, rather than continuously. The system uses the pre-trained variational autoencoders to handle routine adaptations efficiently, and only performs full model updates or cost function re-optimization when triggered by specific conditions, reducing energy consumption while maintaining adaptability.
Data Source
AI summary
A system and method for utilizing model predictive control for optimal interactions that include receiving environment e data associated with a surrounding environment of an ego agent and dynamic data associated with an operation of the ego agent. The system and method also include inputting the environment data and the dynamic data to variational autoencoders. The system and method additionally include utilizing the model predictive control through functional approximation with the variational autoencoders and decoders to output probabilistic action estimates. The system and method further include outputting an estimated optimal control trajectory based on analysis of the probabilistic action estimates to control at least one system of the ego agent to operate within the surrounding environment of the ego agent.