Robot Component Graph Topology for Diverse Message Learning
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Solution Overview
Problem
Existing robot configurations represented by graphs restrict reinforcement learning agents to learning only equivalence messages when components are arranged in parallel, preventing them from learning independence or cooperation messages.
Innovation Solution
A generation device that modifies the arrangement of component groups from parallel to series, generating graphs with specific edge connections to enable agents to learn movements indicated by independence, cooperation, or both types of messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If components are arranged in parallel in the graph, then the equivalence message can be transmitted to multiple nodes, but the agent becomes incapable of learning movements indicated by messages other than equivalence messages
Solution Approach 1:
The patent segments the graph structure into multiple connected components, where each component can have different topological configurations (parallel, series, or mixed). This segmentation allows different message types to be learned in different components, resolving the contradiction between maintaining simple parallel structure and enabling diverse message learning.
Solution Approach 2:
The patent introduces dynamic graph structure selection, where the system can dynamically choose between parallel and series connections based on the desired learning objective. This dynamic configurability enables the agent to learn different message types by adjusting the graph topology, thus improving adaptability without permanently increasing structural complexity.
2Ease of operation
If the graph is generated in the form represented by the hardware configuration of the robot, then the graph structure reflects the physical arrangement, but the agent is restricted to learning only equivalence messages when components are in parallel
Solution Approach 1:
The patent adds a topological dimension to the graph structure, transforming it from a simple parallel representation to a multi-dimensional structure that includes series connections and mixed configurations. This dimensional expansion allows the graph to represent both hardware configuration and diverse movement patterns, resolving the contradiction between ease of generation and learning versatility.
Solution Approach 2:
The patent changes the topological parameters of the graph structure, allowing transitions between parallel and series configurations. By modifying these structural parameters, the system maintains ease of graph generation from hardware configuration while enabling the agent to learn various movement types through parameter-adjusted topologies.
Data Source
AI summary
A generation device includes a generation unit that generates a graph in which a plurality of node groups corresponding to a plurality of component groups arranged in parallel are connected in series or a graph in which a first node corresponding to a first component belonging to a first component group among the plurality of component groups and a second node corresponding to a second component belonging to a second component group among the plurality of component groups are connected to each other via an edge.