Sensitivity Analysis For Building Evolving & Adaptive Robotic Software

kaestner@cs.cmu.edu (P. Kawthekar, C. Kästner)
Wed, 01 Mar 2017 14:35:44 -0500

Prasad Kawthekar, and Christian Kästner. Sensitivity Analysis For Building Evolving & Adaptive Robotic Software. In Proceedings of the IJCAI Workshop on Autonomous Mobile Service Robots (WSR), , July 2016.

There has been a considerable growth in research and development of service robots in recent years. For deployment in diverse environment conditions for a wide range of service tasks, novel features and algorithms are developed and existing ones undergo change. However, developing and evolving the robot software requires making and revising many design decisions that can affect the quality of performance of the robots and that are non-trivial to reason about intuitively because of interactions among them. We propose to use sensitivity analysis to build models of the quality of performance to the different design decisions to ease design and evolution. Moreover, we envision these models to be used for run-time adaptation in response to changing goals or environment conditions. Constructing these models is challenging due to the exponential size of the decision space. We build on previous work on performance influence models of highly-configurable software systems using a machine-learning-based approach to construct influence models for robotic software.