Projects per year
Abstract
Justifying operational decisions for robots is a challenging task as the operator or the robot itself has to understand the underlying physical interaction between the robot and the environment to predict the potential outcome. It is desirable to understand how the decision influences the operational performance in the way of causal relationship for the purpose of explainable decision-making. Here we propose a novel causal inference framework for the discovery and inference on the reasoning of the operational decisions for robots. It unifies both domain knowledge integration and model-free causal inference, allowing a data-driven causal knowledge learning on time series data. The framework is evaluated in the experiments of an underwater robot with complex environmental interactions. The results show that the framework can learn the causal structure and inference model to accurately explain and predict the operation performance with integrated physics.
Original language | English |
---|---|
Title of host publication | 2021 IEEE International Conference on Robotics and Automation, ICRA 2021 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 6124-6131 |
Number of pages | 8 |
ISBN (Electronic) | 9781728190778 |
DOIs | |
Publication status | Published - 18 Oct 2021 |
Event | 2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China Duration: 30 May 2021 → 5 Jun 2021 |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
---|---|
Volume | 2021-May |
ISSN (Print) | 1050-4729 |
Conference
Conference | 2021 IEEE International Conference on Robotics and Automation, ICRA 2021 |
---|---|
Country/Territory | China |
City | Xi'an |
Period | 30/05/21 → 5/06/21 |
Fingerprint
Dive into the research topics of 'Reasoning Operational Decisions for Robots via Time Series Causal Inference'. Together they form a unique fingerprint.Projects
- 1 Finished
-
UK Robotics and Artificial Intelligence Hub for Offshore Energy Asset Integrity Management (ORCA)
Vijayakumar, S., Mistry, M., Ramamoorthy, R. & Williams, C.
1/10/17 → 31/03/22
Project: Research