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Found 4 papers, 2 papers with code
CURE: Simulation-Augmented Auto-Tuning in Robotics
This paper proposes CURE -- a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance.
CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots
Finding the root cause of such faults is challenging due to the exponentially large configuration space and the dependencies between the robot's configuration settings and performance.
Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation.
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots
Modern cyber-physical systems (e. g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time.
