Robot Action Plan Selection for Context-Adaptive Operation
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Solution Overview
Problem
Conventional robots are unable to adaptively perform operations based on varying situations, leading to reduced efficiency in everyday tasks.
Innovation Solution
A robot equipped with a processor that selects from pre-stored, context-generated, or learned action plans based on target and context information, using a look-up table or AI models to determine the most appropriate action plan and update action information for efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot uses predetermined orders for operations, then the control system is simple, but the robot cannot adaptively perform operations according to various situations
Solution Approach 1:
The patent segments the control system into three distinct modules: a first module for executing predetermined action plans, a second module for generating action plans based on context information using AI, and a third module for learning and storing action plans based on operation patterns. This segmentation allows the robot to handle different situations through specialized modules, improving adaptability while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent implements dynamic switching between different action plan execution modes based on the situation. The controller dynamically selects whether to use predetermined action plans, AI-generated action plans, or learned action plans depending on the context information and operation patterns. This dynamic approach enables the robot to adapt to varying situations without requiring a completely complex control system for all scenarios simultaneously.
2Adaptability or versatility
If the robot generates action information based on context information, then the robot can adapt to new situations, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-storing multiple action plans in the controller and pre-training AI models for generating action plans. When a situation arises, the robot can quickly retrieve pre-prepared action plans or use pre-trained AI models, avoiding the need to generate action information from scratch. This preliminary preparation significantly reduces processing time while maintaining adaptability to new situations.
Solution Approach 2:
The patent uses copying by storing learned operation patterns and action plans in memory for future reference. Instead of generating entirely new action information for each situation, the robot copies and adapts previously successful action plans and operation patterns. This copying approach reduces computational resources and time requirements while maintaining adaptability through pattern recognition and reuse.
3Productivity
If the robot uses learned operation patterns, then the efficiency of repeated tasks improves, but the system requires more memory and learning capacity
Solution Approach 1:
The patent implements self-service by enabling the robot to automatically learn and store its own operation patterns through the third module. The system autonomously captures operation patterns from executed tasks, processes them, and stores them in memory for future use. This self-learning capability improves productivity for repeated tasks while managing memory requirements through automated pattern recognition and selective storage of meaningful operation patterns.
Data Source
AI summary
A includes a driver, and a processor configured to: based on an occurrence of an event for performing an operation, acquire target information corresponding to the operation and context information related to the robot, based on at least one of the target information or the context information, select an action plan, acquire action information based on the action plan, and control the driver such that an operation corresponding to the target information is performed based on the action information. The action plan is selected as a first action plan of performing an operation according to first action information stored in advance in the memory, a second action plan of performing an operation according to second action information generated based on the acquired target information and the acquired context information, and a third action plan of performing an operation according to third action information learned based on an operation pattern of the robot.