• 1/24 — I received an NIH Trailblazer award in collaboration with Prof. Haohan Zhang to develop intelligent and adaptive control of a powered neck exoskeleton!
• 12/23 — Our paper Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning was accepted to HRI 2024!
• 9/23 — Our paper Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots was accepted to MRS 2023!
• 9/23 — Our paper Player-Centric Procedural Content Generation: Enhancing Runtime Customization by Integrating Real-Time Player Feedback was accepted as a work-in-progress to CHI PLAY 2023!
• 8/23 — Our paper Quantifying Assistive Robustness Via the Natural-Adversarial Frontier was accepted to CoRL 2023!
• 3/23 — Our paper Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms was accepted to GECCO 2023!
• 11/25/22 — Our paper The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types was accepted to AAAI 2023!
• 11/14/22 — Our paper Interpretable Reward Learning via Differentiable Decision Trees has been accepted to the NeurIPS 2022 ML Safety Workshop!
• 11/14/22 — Our paper "Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models" has been accepted to the NeurIPS 2022 Workshop on Human in the Loop Learning!
• 9/14/22 — Our paper Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations has been accepted to NeurIPS 2022!
• 9/10/22 — Our paper Learning Representations that Enable Generalization in Assistive Tasks has been accepted to CoRL 2022!
• 8/13/22 — Initial course website is up for my Fall 2022 class: Human-AI Alignment.
• 8/2/22 — Our paper Teaching Robots to Span the Space of Functional Expressive Motion was accepted to IROS 2022.
• 7/1/22 — I started as an assistant professor at the University of Utah's Robotics Center and School of Computing.
• 5/25/22 — Our paper Study of Causal Confusion in Preference-Based Reward Learning has been accepted to the RSS 2022 Workshop Overlooked Aspects of Imitation Learning: Systems, Data, Tasks, and Beyond.
• 5/25/22 — Our paper Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies was accepted to CASE 2022.
• 5/6/22 — I gave at talk at the Stanford Robotics Seminar on "Leveraging Human Input to Enable Robust AI Systems".
• 3/30/22 — I accepted a position as an assistant professor at the School of Computing and Robotics Center at the University of Utah starting all 2022!
• 1/31/21 — Our paper LEGS: Learning Efficient Grasp Sets for Exploratory Grasping was accepted at ICRA 2022!
• 10/15/21 — I gave an AI Seminar on "Efficient and Robust Learning of Human Intent" at U. Alberta. Video here.
• 10/1/21 — I was an invited speaker on the Talking Robotics virtual seminar series. Video here.
• 9/15/21 — Our paper ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning was accepted to CoRL 2021.
• 8/10/21 — Our workshop on Safe and Robust Control of Uncertain Systems was accepted to NeurIPS 2021.
• 6/14/21 — Our paper on Offline Preference-Based Apprenticeship Learning was accepted at the ICML Workshop on Human-AI Collaboration in Sequential Decision-Making.
• 6/10/21 — I was selected as a 2021 RSS Pioneer.
• 6/4/21 — Two papers accepted to IEEE CASE 2021: LazyDAgger: Reducing Context Switching in Interactive Imitation Learning and Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities.
• 5/29/21 — Two papers accepted to ICML 2021: Value Alignment Verification and Policy Gradient Bayesian Robust Optimization for Imitation Learning.
• 5/20/21 — I gave a talk at UC Berkeley's Midday Science Cafe on "Harnessing Machine Learning for Science," with a focus on how robots can learn from, model, and better assist humans. Video recording here.
• 3/15/21 — Our paper Optimal Cost Design for Model Predictive Control was accepted at the 3rd Annual Learning for Dynamics & Control Conference (L4DC).
• 2/28/21 — Two papers accepted at ICRA 2021: Dynamically Switching Human Prediction Models for Efficient Planning and Situational Confidence Assistance for Lifelong Shared Autonomy.
• 10/31/20 — Our paper Value Alignment Verification was accepted at the NeurIPS 2020 HAMLETS Workshop!
• 10/14/20 — Our paper Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects was accepted at CoRL 2020!
• 9/25/20 — Our paper on Bayesian Robust Optimization for Imitation Learning was accepted at NeurIPS 2020!
• 8/31/20 — I started a postdoc at UC Berkeley working with Anca Dragan and Ken Goldberg.
• 7/20/20 — I successfully defended my PhD dissertation! Video of presentation here.
• 5/31/20 — Our paper Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences was accepted to ICML 2020.
• 2/01/20 — My research with Prof. Scott Niekum was featured in a recent Quanta magazine article.
• 11/18/19 — I passed my PhD dissertation proposal and advanced to candidacy!
• 10/10/19 — Our paper Deep Bayesian Reward Learning from Preferences was accepted to the 2019 NeurIPS Workshop on Safety and Robustness in Decision Making.
• 9/7/19 — Our paper Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations was accepted to the 2019 Conference on Robot Learning (CoRL).
• 6/1/19 — Code for our paper Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations is available on github: T-REX code. We also have a project page with videos: T-REX Project Page.
• 4/21/19 — Our paper on Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations was accepted to ICML 2019.
• 4/12/19 — Our paper on Learning from Suboptimal Demonstrations through Inverse Reinforcement Learning from Ranked Observations was accepted to RLDM 2019.
• 10/31/18 — Our paper on Machine Teaching for Inverse Reinforcement Learning accepted to AAAI 2019.
• 9/1/18 — Our paper on Risk-Aware Active Inverse Reinforcement Learning accepted to the 2018 Conference on Robot Learning.
• 8/17/18 — Our 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 — I 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 — I presented our paper on Probabilistic Safety Bounds for Robot Learning from Demonstration at the AAAI Fall Symposium on AI for HRI.
• 11/9/17 — Our paper on Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning was accepted to AAAI 2018.
• 7/30/17 — Our team UT Austin Villa won third place in the 2017 Robocup@Home Domestic Standard Platform League in Nagoya, Japan.