Robot Skill Tuples for Temporal Logic Motion Commands
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Solution Overview
Problem
Existing technologies fail to accurately generate operation commands for robot motion planning, particularly in complex tasks involving multiple object interactions or contacts, such as assembly tasks, as they do not effectively utilize temporal logic formulas.
Innovation Solution
An operation command generation device and method that acquires skill information, generates skill tuples, and produces temporal logic commands to represent robot operations, incorporating skill use operation commands and evaluation functions for optimized motion planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional robot control methods are used for complex assembly tasks, then the robot can execute basic tasks, but it cannot accurately generate operation commands for complex tasks involving multiple object interactions
Solution Approach 1:
The patent segments complex robot tasks into atomic skills with well-defined preconditions and postconditions. Each skill represents a fundamental operation (e.g., grasp, place, push) that can be independently verified and combined. This segmentation enables accurate operation command generation by ensuring each component skill is correctly specified before composition.
Solution Approach 2:
The patent introduces temporal logic formulas as an intermediary layer between task specifications and robot execution. These formulas serve as mediators that formally capture the temporal and logical relationships between atomic skills, enabling precise representation of complex task requirements involving multiple object interactions.
2Measurement precision
If temporal logic formulas are generated for complex motion planning, then operation accuracy improves, but the calculation process becomes more complex
Solution Approach 1:
The patent divides the complex temporal logic generation process into manageable components: skill definition, skill tuple generation, and skill use operation command generation. Each component handles a specific aspect of the problem, reducing overall calculation complexity while maintaining precision.
Solution Approach 2:
The patent changes the parameters of skill representation by using structured skill tuples with explicit preconditions and postconditions. This parameterization allows the temporal logic formulas to be generated systematically from these structured representations, simplifying the calculation process while maintaining operational precision.
3Adaptability or versatility
If skill tuples are generated from skill information, then the system can represent complex operations, but the data processing requirements increase
Solution Approach 1:
The patent applies local quality by making skill information highly structured and localized to specific atomic operations. Each skill tuple contains only the necessary preconditions and postconditions relevant to that specific skill, avoiding unnecessary data while enabling comprehensive representation of complex operations through composition.
Data Source
AI summary
The operation command generation device 1Y mainly includes a skill information acquisition means 341Y, a skill tuple generation means 342Y, and a skill use operation command generation means 343Y. The skill information acquisition means 341Y is configured to acquire skill information relating to a skill to be used in a motion planning of a robot. The skill tuple generation means 342Y is configured to generate, based on the skill information, a skill tuple which is a set of variables in a system model, the variables being associated with the skill, the system model being set in the motion planning. The skill use operation command generation means 343Y is configured to generate a skill use operation command that is a temporal logic command representing an operation corresponding to the skill tuple.