Student Talks
A Modularized Approach to Vision-based Tactile Sensor Design Using Physics-based Rendering
Abstract: Touch is an essential sensing modality for making autonomous robots more dexterous and allowing them to work collaboratively with humans. In particular, the advent of vision-based tactile sensors has resulted in efforts to design them for different robotic manipulation tasks. However, this design task remains a challenging problem. This is for two reasons: first, [...]
Towards Universal Place Recognition
Title: Towards Universal Place Recognition Abstract: Place Recognition is essential for achieving robust robot localization. However, current state-of-art systems remain environment/domain-specific and fragile. By leveraging insights from vision foundation models, we present AnyLoc, a universal VPR solution that performs across diverse environments without retraining or fine-tuning, significantly outperforming supervised baselines. We further introduce MultiLoc, and enable [...]
Enhancing Model Performance and Interpretability with Causal Inference as a Feature Selection Algorithm
Abstract: Causal inference focuses on uncovering cause-effect relationships from data, diverging from conventional machine learning which primarily relies on correlation analysis. By identifying these causal relationships, causal inference improves feature selection for predictive models, leading to predictions that are more accurate, interpretable, and robust. This approach proves especially effective with interventional data, such as randomized [...]