Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots

Simón C. Smith, Subramanian Ramamoorthy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Understanding a controller’s performance in different scenarios is crucial for robots that are going to be deployed in safety-critical tasks. If we do not have a model of the dynamics of the world, which is often the case in complex domains, we may need to approximate a performance function of the robot based on its interaction with the environment. Such a performance function gives us insights into the behaviour of the robot, allowing us to fine-tune the controller with manual interventions. In high-dimensionality systems, where the action state space is large, fine-tuning a controller is non-trivial. To overcome this problem, we propose a performance function whose domain is defined by external features and parameters of the controller. Attainment regions are defined over such a domain defined by feature-parameter pairs, and serve the purpose of enabling prediction of successful execution of the task. The use of the feature-parameter space –in contrast to the action-state space– allows us to adapt, explain and finetune the controller over a simpler (i.e., lower dimensional) space. When the robot successfully executes the task, we use the attainment regions to gain insights into the limits of the controller, and its robustness. When the robot fails to execute the task, we use the regions to debug the controller and find adaptive and counterfactual changes to the solutions. Another advantage of this approach is that we can generalise through the use of Gaussian processes regression of the performance function in the high-dimensional space. To test our approach, we demonstrate learning an approximation to the performance function in simulation, with a mobile robot traversing different terrain conditions. Then, with a sample-efficient method, we propagate the attainment regions to a physical robot in a similar environment.
Original languageEnglish
Title of host publication2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages6546-6551
Number of pages6
ISBN (Electronic)978-1-6654-1714-3
ISBN (Print)978-1-6654-1715-0
DOIs
Publication statusPublished - 16 Dec 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems - Online, Prague, Czech Republic
Duration: 27 Sept 20211 Oct 2021
https://www.iros2021.org/

Publication series

Name
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2021
Country/TerritoryCzech Republic
CityPrague
Period27/09/211/10/21
Internet address

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