- 11/10/17 — Code for our AAAI 2018 paper is now available on github: aaai-2018-code
- 11/10/17 — Presented our paper at AAAI 2018 in New Orleans. We develop a practical method for bounding policy loss and performing risk-aware policy improvement when learning from demonstration. Presentation slides available as PowerPoint or PDF.
- 11/10/17 — Presented paper on Probabilistic Safety Bounds for Robot Learning from Demonstration at the AAAI Fall Symposium on AI for HRI.
- 11/9/17 — AAAI 2018 paper accepted on Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning.
- 7/30/17 — Our team UT Austin Villa won third place in the 2017 Robocup@Home Domestic Standard Platform League in Nagoya, Japan.
I'm a second-year computer science PhD Student at UT Austin. I work in the Personal Autonomous Robotics Lab (PeARL) with Scott Niekum, where I'm researching safe learning from demonstration. In particular, I'm developing methods that allow a robot to reason about the safety and performance a policy learned from a limited number of demonstrations.