Action Recommendation Engine for Shared Robot Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing leg type mobile robots struggle to efficiently share learning results with other autonomous mobile bodies, limiting their ability to improve learning processes.
Innovation Solution
An information processing apparatus and method that includes an action recommendation unit, which presents recommended actions to autonomous mobile bodies based on an action history from multiple robots and situation summaries from target robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous mobile bodies perform independent learning, then each body can autonomously improve its actions, but learning results cannot be shared with other bodies
Solution Approach 1:
An information processing apparatus acts as an intermediary between autonomous mobile bodies. This mediator collects action histories from multiple bodies, generates situation summaries, and creates recommended actions that are then transmitted back to the autonomous bodies, enabling indirect knowledge sharing without requiring direct body-to-body communication
Solution Approach 2:
The system creates copies of learning results by generating situation summaries from action histories and recommended actions that represent learned knowledge. These copied knowledge representations are distributed to multiple autonomous mobile bodies, allowing them to benefit from each other's learning without direct neural connection
2Measurement precision
If action recommendations are based on comprehensive action history, then recommendation quality improves, but information processing complexity increases
Solution Approach 1:
The system extracts only the essential elements needed for recommendation generation from the complete action history. It identifies relevant situations and actions, creates condensed situation summaries, and generates focused recommended actions, thereby reducing processing complexity while maintaining recommendation quality
Solution Approach 2:
The information processing is segmented into distinct functional modules: action history collection, situation summary generation, recommended action creation, and transmission. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while processing comprehensive data
Data Source
AI summary
There is provided an information processing apparatus and an information processing method that can provide more useful information for an action plan of an autonomous mobile body, the information processing apparatus including an action recommendation unit configured to present a recommended action recommended to an autonomous mobile body, to the autonomous mobile body that performs an action plan based on situation estimation. The action recommendation unit determines the recommended action on the basis of an action history collected from a plurality of the autonomous mobile bodies, and on the basis of a situation summary received from a target autonomous mobile body that is a target of recommendation. The information processing method includes presenting, by a processor, a recommended action recommended to an autonomous mobile body, to the autonomous mobile body that performs an action plan based on situation estimation.