Welcome to the Laboratory for Learning Evaluation of autoNomous Systems (LENS Lab). We are interested in operationalizing machine learning (ML) models in autonomous systems and robots. Our objective is to develop a range of algorithmic tools for evaluating ML models utilized in such systems. We firmly believe that assessing models solely based on accuracy on a held-out dataset is insufficient for successful operationalization. Therefore, we delve deeper into critical questions: How can we explain black-box neural networks? How can we evaluate uncertainty of decisions? When and how do models fail? Are models fair and unbiased? Will a model perform effectively in new or changing environments? We examine these questions before, during, and after deployment. These efforts not only aid engineers in debugging models but also assist legislative bodies in establishing legal and ethical guidelines.
- May'24 - Ransalu wrote a new book chapter titled The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning.
- May'24 - Som's paper titled Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models got accepted to ICML 2024 as a spotlight-designated paper 🎗 (top 3.5%).
- August'23 - Ransalu is co-organizing the 6th Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models at NeurIPS 2023.
- August'23 - Ransalu joined Arizona State University (ASU) as an Assistant Professor. He is currently accepting PhD students with a strong track record in machine learning, robotics, computer vision, or natural language processing research to conduct core machine learning or/and robotics research. Additionally, master's and undergraduate students who have already enrolled in an ASU program can also apply to the LENS lab. See Join Us! for more information.