149 citations · 270 across the 11 of their papers we have counts for
12 papers
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
Cole Gulino, Justin Fu, Wenjie Luo +19
Simulation is an essential tool to develop and benchmark autonomous vehicle planning software in a safe and cost-effective manner. However, realistic simulation requires accurate m…
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
Yiren Lu, Justin Fu, George Tucker +9
Imitation learning (IL) is a simple and powerful way to use high-quality human driving data, which can be collected at scale, to produce human-like behavior. However, policies base…
Deep Reinforcement Learning in a Monetary Model
Mingli Chen, Andreas Joseph, Michael Kumhof +2
We propose using deep reinforcement learning to solve dynamic stochastic general equilibrium models. Agents are represented by deep artificial neural networks and learn to solve th…
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects
Xinlei Pan, Animesh Garg, Animashree Anandkumar +1
Evolution in nature illustrates that the creatures' biological structure and their sensorimotor skills adapt to the environmental changes for survival. Likewise, the ability to mor…
Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation
Xinlei Pan, Tingnan Zhang, Brian Ichter +3
Imitation learning is a popular approach for training visual navigation policies. However, collecting expert demonstrations for legged robots is challenging as these robots can be…
Characterizing Attacks on Deep Reinforcement Learning
Xinlei Pan, Chaowei Xiao, Warren He +8
Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations.…
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
Xinlei Pan, Weiyao Wang, Xiaoshuai Zhang +3
Machine learning has been widely applied to various applications, some of which involve training with privacy-sensitive data. A modest number of data breaches have been studied, in…
Risk Averse Robust Adversarial Reinforcement Learning
Xinlei Pan, Daniel Seita, Yang Gao +1
Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the tra…
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
Xinlei Pan, Sung-Li Chiang, John Canny
Self-driving vehicle vision systems must deal with an extremely broad and challenging set of scenes. They can potentially exploit an enormous amount of training data collected from…
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
Xinlei Pan, Eshed Ohn-Bar, Nicholas Rhinehart +3
Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequ…
Virtual to Real Reinforcement Learning for Autonomous Driving
Xinlei Pan, Yurong You, Ziyan Wang +1
Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environm…
An Efficient Minibatch Acceptance Test for Metropolis-Hastings
Daniel Seita, Xinlei Pan, Haoyu Chen +1
We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data. Previous work on reducing the cost of Metropolis-Hastings tests…