Constrained Control Input Exploration for Continuous Safety Limits
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Solution Overview
Problem
Existing methods for controlling computer-controlled systems, such as robots and manufacturing systems, face challenges in efficiently learning about safety constraints without violating them, especially when dealing with continuous-valued control inputs and requiring fewer hyperparameters.
Innovation Solution
The method employs Information-Theoretic Safe Exploration (ISE) by determining control inputs based on mutual information between the constraint quantity and other control inputs, optimizing for maximal information gain about safety without relying on discrete domains or Lipschitz constants, allowing for efficient exploration of the safe parameter space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing safe exploration methods (e.g., SafeOpt) are used to learn about safety constraints, then safety can be maintained, but data efficiency is poor and the methods require tuning of hyperparameters like Lipschitz constants
Solution Approach 1:
The patent changes the fundamental parameter for exploration selection from uncertainty-based metrics (used in SafeOpt) to mutual information-based metrics. This parameter change enables the system to select exploration points that maximize information gain about safety constraints while maintaining safety, thereby improving data efficiency without sacrificing safety guarantees
Solution Approach 2:
The patent introduces mutual information as an intermediary metric that bridges the gap between exploration and safety. Instead of directly selecting points based on uncertainty (which may lead to unsafe explorations), the mutual information metric serves as a mediator that quantifies the expected information gain about safety constraints while inherently accounting for safety considerations
2Reliability
If discrete domain methods are used for safe exploration, then safety can be ensured, but the methods cannot efficiently handle continuous-valued control inputs
Solution Approach 1:
The patent makes the exploration strategy dynamic by using mutual information to adaptively select continuous-valued control inputs based on the current state of knowledge about safety constraints. This dynamic approach allows the system to efficiently explore continuous parameter spaces while maintaining safety guarantees, overcoming the limitations of static discrete domain methods
Solution Approach 2:
The patent replaces the mechanical discretization approach with an information-theoretic continuous optimization approach. Instead of dividing the continuous space into discrete regions and exploring them separately, the system uses mutual information to directly optimize over continuous control inputs, thereby maintaining both safety guarantees and efficiency in continuous domains
3Ease of operation
If Lipschitz continuity assumptions are made to enable safe exploration, then discrete domain exploration can proceed, but the methods become complex and require multiple hyperparameters
Solution Approach 1:
The patent extracts and removes the Lipschitz continuity assumption from the safe exploration framework. By using mutual information as the basis for exploration selection, the system no longer requires Lipschitz constants or other complex regularity assumptions, thereby simplifying the method and reducing the number of hyperparameters that need to be tuned while maintaining safe exploration capability
Data Source
AI summary
A computer-implemented control method of constrained controlling of a computer-controlled system. The system is controlled according to a control input, which is safe if a constraint quantity resulting from the controlling of the computer-controlled system exceeds a constraint threshold. A current control input is determined based on previous control inputs and corresponding previous noisy measurements. The computer-controlled system is controlled according to the current control input, thereby obtaining a current noisy measurement of the resulting constraint quantity. The current control input is determined based on a mutual information between a first random variable representing the constraint quantity resulting from the current control input and a second random variable indicating whether a further control input is safe.