3 papers
cs.RO2026
UfM*: Uncertainty from Motion* for DNN Depth Estimation Using Gaussians
Soumya Sudhakar, Sertac Karaman, Vivienne Sze
Reliable uncertainty estimation is critical for deploying monocular depth deep neural networks (DNNs) in safety-critical robotic systems. Conventional uncertainty methods such as e…
cs.RO2025
A Roadmap for Climate-Relevant Robotics Research
Alan Papalia, Charles Dawson, Laurentiu L. Anton +25
Climate change is one of the defining challenges of the 21st century, and many in the robotics community are looking for ways to contribute. This paper presents a roadmap for clima…
cs.LG2025
DecTrain: Deciding When to Train a Monocular Depth DNN Online
Zih-Sing Fu, Soumya Sudhakar, Sertac Karaman +1
Deep neural networks (DNNs) can deteriorate in accuracy when deployment data differs from training data. While performing online training at all timesteps can improve accuracy, it…