Rather than crossing our fingers and hoping a robot has enough demonstration data, we want robots that can confidently self-assess their learning and know when their policy performance is close to the demonstrator's. Congrats to Tu Trinh for a best-paper nomination at HRI!

Great work by Dimitri on scaling up Bayesian constraint learning!

Congrats to Zohre karimi, Shing-Hei Ho, and Bao Thach on their paper being a best student paper award finalist and best overall paper award finalist at the International Symposium on Medical Robotics (ISMR) this week!

Really excited about our recent work on interpretable reward learning!

This was a fun project on reward and representation learning from mixed-quality, mixed-embodiment data! If you're at RLC, check out Connor Mattson's talk and poster tomorrow!

Great postdoc opportunity in Responsible AI!

Great article on our recent ARPA-H collaboration on automation for surgical robots. Super excited to work with an all-star team on this problem, especially on the areas of uncertainty quantification, learning from human feedback, and lifelong learning: price.utah.edu/2024/09/30/kah…

We're hiring in robotics and human-robot interaction! Join our faculty at the Kahlert School of Computing at University of Utah ! cra.org/job/university…

Excited to announce that I've been invited to give a talk at AAAI-25 on "Leveraging Human Input to Enable Robust, Interactive, and Aligned AI Systems" as part of their New Faculty Highlights program!

📢Exciting news! Our workshop Human-in-the-Loop Robot Learning: Teaching, Correcting, and Adapting has been accepted to RSS 2025!🤖🎉Join us as we explore how robots can learn from and adapt to human interactions and feedback. 🔗Workshop website: hitl-robot-learning.github.io 🧵👇

Super proud of my students and their great work that was showcased last week at AAMAS!