Robotic Task Control Using STL Constraints and Black-Box Optimization
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Solution Overview
Problem
Existing robotic control methods, such as Learning from Demonstrations (LfD), struggle to define explicit conditions beyond their implicit capabilities, limiting the range of conditions that can be optimized, particularly in complex tasks like assembly where precise force control and spatial constraints are necessary.
Innovation Solution
A method using black box optimization (BBO) with signal temporal logic (STL) to adjust robot control models, allowing for a wider range of condition definitions by optimizing parameters based on continuous sensor signals, incorporating hidden semi-Markov models (HSMM) to ensure compliance with spatial and temporal constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optimization techniques are used to improve movement skills with explicit conditions, then task performance can be enhanced, but the range of definable conditions is limited due to gradient requirements
Solution Approach 1:
The patent replaces gradient-based optimization (mechanical/mathematical system requiring differentiability) with black box optimization. This substitution allows the use of STL conditions that do not require gradient computation, expanding the range of definable conditions while maintaining optimization capability for precise task performance.
Solution Approach 2:
The patent changes the optimization approach from gradient-based parameter adjustment to black box optimization. This parameter change in the optimization methodology enables the system to handle a broader class of conditions (STL specifications) that are not restricted by differentiability requirements, thus expanding the range of definable conditions.
2Adaptability or versatility
If black box optimization is used to optimize the target function, then the range of definable conditions expands significantly, but the optimization process becomes more computationally intensive
Solution Approach 1:
The patent performs preliminary action by formulating conditions in STL formalism before optimization. This pre-processing step structures the conditions in a way that enables efficient black box optimization evaluation, reducing computational overhead during the optimization process while maintaining the expanded range of definable conditions.
3Measurement precision
If continuous sensor signals are used to evaluate adherence to conditions, then the temporal progression of signals can be precisely monitored, but the complexity of the control system increases
Solution Approach 1:
The patent introduces STL robustness metrics as an intermediary between continuous sensor signals and condition evaluation. These metrics serve as a bridge that translates complex temporal signal analysis into simplified compliance assessments, maintaining measurement precision while reducing control system complexity.
Data Source
AI summary
A method of controlling a robotic device. The method includes generating a robot control model for performing a task, wherein the robot control model comprises parameters which influence the performance of the task, adjusting the parameters of the robot control model by optimizing a target function which evaluates the adherence to at least one condition with respect to the temporal progression of at least one continuous sensor signal when performing the task, and controlling the robotic device according to the robot control model in order to perform the task using the adjusted parameters.