- 10/31/18 — Paper on Machine Teaching for Inverse Reinforcement Learning accepted to AAAI 2019.
- 9/1/18 — Paper on Risk-Aware Active Inverse Reinforcement Learning accepted to the 2018 Conference on Robot Learning.
- 8/17/18 — Paper on our UT Austin Villa Robocup@Home Robot Architecture accepted to the AAAI 2018 Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy.
- 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 third-year computer science PhD Student at UT Austin. I work in the Personal Autonomous Robotics Lab (PeARL) with Scott Niekum, where I'm researching learning from demonstration with an emphasis on inverse reinforcement learning and safety. 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, ask questions to resolve ambiguities in human demonstrations, and learn from demonstrations that aren't i.i.d..