Projects per year
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.
|Title of host publication||2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||6|
|Publication status||Published - 16 Dec 2021|
|Event||2021 IEEE/RSJ International Conference on Intelligent Robots and Systems - Online, Prague, Czech Republic|
Duration: 27 Sep 2021 → 1 Oct 2021
|Conference||2021 IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Abbreviated title||IROS 2021|
|Period||27/09/21 → 1/10/21|
FingerprintDive into the research topics of 'Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots'. Together they form a unique fingerprint.
- 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