Adam Villaflor

I recently graduated with a PhD from the Robotics Institute at Carnegie Mellon University, where I had the good fortune to be co-advised by Professors Jeff Schneider and John Dolan.

My research interests are focused on deep reinforcement learning, generative modeling, and deep learning for prediction and planning. I am excited about developing and deploying machine learning systems for real-world applications like autonomous driving. I received my Bachelor's and Master's at UC Berkeley, where I did research in reinforcement learning for robotics as part of Professors Sergey Levine's and Pieter Abbeel's group.

Email / CV / Google Scholar / LinkedIn

Research

	Tractable Joint Prediction and Planning Over Discrete Behavior Modes for Urban Driving Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John Dolan, Jeff Schneider ICRA, 2024 paper / code
	Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning Adam Villaflor, Zhe Huang, Swapnil Pande, John Dolan, Jeff Schneider ICML, 2022 paper / code
	BATS: Best Action Trajectory Stitching Ian Char, Viraj Mehta, Adam Villaflor, John Dolan, Jeff Schneider NeurIPS Offline Reinforcement Learning Workshop, 2021 paper
	Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios Christoph Killing, Adam Villaflor, John Dolan ICRA, 2021 paper
	Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization Adam Villaflor, John Dolan, Jeff Schneider NeurIPS Offline Reinforcement Learning Workshop, 2020 paper
	Learning Highway Ramp Merging via Reinforcement Learning with Temporally-Extended Actions Samuel Triest, Adam Villaflor, John Dolan IV, 2020 paper
	Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation Gregory Kahn, Adam Villaflor, Pieter Abbeel, Sergey Levine CoRL, 2018 paper / code
	Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine ICRA, 2018 paper / code
	Uncertainty-Aware Reinforcement Learning for Collision Avoidance Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, Sergey Levine arXiv, 2017 paper

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