![[RAL2025] Video Foundation Models to Enhance Intermittent Supervision @AutonomousSystemsLab](https://i.ytimg.com/vi/sLUD1xTIeFs/maxresdefault.jpg)
Uploaded May 2025 | Updated September 2026, 1 week ago
K. Katuwandeniya, L. Tian and D. Kulić, "‘What Did the Robot Do in My Absence?’ Video Foundation Models to Enhance Intermittent Supervision," in IEEE Robotics and Automation Letters, vol. 10, no. 4, pp. 3222-3229, April 2025, doi: 10.1109/LRA.2025.3539118.
For more information, please check out the project website: kavindie.github.io/what-did-the-robot-do-in-my-absence
K. Katuwandeniya, L. Tian and D. Kulić, "‘What Did the Robot Do in My Absence?’ Video Foundation Models to Enhance Intermittent Supervision," in IEEE Robotics and Automation Letters, vol. 10, no. 4, pp. 3222-3229, April 2025, doi: 10.1109/LRA.2025.3539118.
For more information, please check out the project website: kavindie.github.io/what-did-the-robot-do-in-my-absence
![[RAL2025] Video Foundation Models to Enhance Intermittent Supervision](https://i.ytimg.com/vi/sLUD1xTIeFs/mqdefault.jpg "[RAL2025] Video Foundation Models to Enhance Intermittent Supervision
K. Katuwandeniya, L. Tian and D. Kulić, ‘What Did the Robot Do in My Absence?’ Video Foundation Models to Enhance Intermittent Supervision, in IEEE Robotics and Automation Letters, vol. 10, no. 4, pp. 3222-3229, April 2025, doi: 10.1109/LRA.2025.3539118.
For more information, please check out the project website: https://kavindie.github.io/what-did-the-robot-do-in-my-absence/")
![[IROS2021] CatChatter: Acoustic Perception for Mobile Robots](https://i.ytimg.com/vi/uLSE8__TCHQ/mqdefault.jpg "[IROS2021] CatChatter: Acoustic Perception for Mobile Robots
There are many examples in nature of animals using acoustics to understand and navigate the world around them. Inspired by this, we train an image-to-image translation network to learn the mapping from recorded chirps and echos from an environment to a 360 degree depth map of the environment. This work is focused on expanding on the capabilities of previously published BatVision in a number of ways. We first propose various methods for data augmentation to help the model generalise on less data. We also propose changes to the model architecture to improve performance and training stability. Finally, we investigate the feasibility of 360 degree scene reconstruction by using more microphones and lidar based 3D SLAM data as ground truth for training the model.
You can find the full IEEE RA-L paper here: https://ieeexplore.ieee.org/document/9472944")
