Autonomous Machine Cluster Layout With Adaptive Policy Learning
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Solution Overview
Problem
Current systems face challenges in enabling seamless collaboration and coordination among autonomous machines across clusters, particularly in learning Reinforcement Learning policies and integrating AI/ML models for multi-robot systems, due to complexities in state-space dimensionality and sparse rewards, as well as the need for specialized knowledge and tools.
Innovation Solution
The implementation of an AI/ML-based system that allows for on-the-fly policy learning and adaptive planning, using a processor with AI/ML and policy optimization modules to determine optimal layouts and adjust policy parameters for autonomous machine clusters, enabling efficient task performance and inter-cluster coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Reinforcement Learning policies are learned for autonomous machine collaboration, then task performance efficiency is improved, but state-space dimensionality complexity increases
Solution Approach 1:
The patent segments the complex collaborative task into multiple sub-tasks, with each autonomous machine learning specialized policies for its specific sub-task rather than a single monolithic policy for the entire task. This division reduces the state-space dimensionality each machine must handle while maintaining overall task performance efficiency.
Solution Approach 2:
The patent introduces a hierarchical dimension to the policy learning architecture, where high-level coordination policies operate at one level and low-level execution policies operate at another level. This dimensional transformation reduces the complexity of state-space by distributing policy learning across multiple hierarchical levels rather than requiring a single comprehensive policy.
2Adaptability or versatility
If AI/ML models are integrated for multi-robot systems, then adaptability is improved, but specialized knowledge requirements increase
Solution Approach 1:
The patent implements self-service through automated policy learning and model integration capabilities that allow the system to adapt to new tasks and environments without requiring extensive manual programming or specialized expertise. The autonomous machines automatically learn collaboration policies and integrate AI/ML models through standardized interfaces, reducing dependency on specialized knowledge while maintaining high adaptability.
3Reliability
If operation policies are optimized for cluster coordination, then inter-cluster coordination is improved, but computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training policy models and performing computationally intensive optimization operations before deployment. Operation policies are learned and refined in advance through simulation and training phases, allowing the autonomous machines to execute coordinated tasks with reduced real-time computational requirements and lower energy consumption during actual operation.
Data Source
AI summary
A device may include a processor configured to determine a layout for a plurality of automated machine clusters to be deployed in an environment based on a plurality of operation policies and an input task, wherein each operation policy provides a policy to operate one or more automated machines of one of the plurality of automated machine clusters for a trained task based on one or more policy parameters. The processor may further be configured to adjust the one or more policy parameters of at least one of the plurality of operation policies based on the determined layout in the environment and the input task.