Nolan Wagener

I am a final-year PhD candidate in robotics at the Georgia Institute of Technology, where I am advised by Byron Boots (UW) and Panagiotis Tsiotras. I am interested in learning for robotics, particularly in allowing robots to safely and efficiently learn tasks. I am fortunate to have been funded by the NSF Graduate Research Fellowship and received and been nominated for several "best paper" awards at robotics conferences.

I am currently in the industry job market for a position in cutting-edge research in robotics and machine learning.

Email  /  CV  /  GitHub  /  Google Scholar  /  LinkedIn

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TerrainNet: Visual Modeling of Complex Terrain for High-Speed, Off-Road Navigation

Xiangyun Meng, Nathan Hatch, Alexander Lambert, Anqi Li, Nolan Wagener, Matthew Schmittle, JoonHo Lee, Wentao Yuan, Zoey Chen, Samuel Deng, Greg Okopal, Dieter Fox, Byron Boots, Amirreza Shaban
Robotics: Science and Systems (RSS), 2023
Paper  /  Website  /  Talk  /  Poster  /  Slides

A vision-based system for off-road driving that can predict semantic and geometric terrain information from stereo camera input.

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MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control

Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri, Ricky Loynd, Ching-An Cheng, Matthew Hausknecht
Neural Information Processing Systems (NeurIPS), 2022
Paper  /  Website  /  Blog  /  Talk  /  Short Talk  /  Code  /  Dataset  /  Poster  /  Slides

We release a dataset of high-quality experts and their rollouts for tracking 3.5 hours of MoCap data in dm_control. We use this dataset to train policies that can track the entire dataset, efficiently transfer to other tasks, and perform physics-based motion completion.

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DARPA Robotic Autonomy in Complex Environments with Resiliency (RACER)

University of Washington, 2021
Website  /  Article  /  Video

High-speed off-road autonomy in complex terrain. I led development of a control stack that enables a Polaris RZR vehicle to traverse a wide variety of unstructured environments and terrains.

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Safe Reinforcement Learning Using Advantage-Based Intervention

Nolan Wagener, Byron Boots, Ching-An Cheng
International Conference on Machine Learning (ICML), 2021
Paper  /  Talk  /  Code  /  Poster  /  Slides

An intervention-based technique for safe reinforcement learning which is based on an advantage function estimate with respect to a given baseline policy. Our work comes with strong theoretical guarantees on performance after training and safety both during and after training, which we corroborate with simulated experiments.

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Scalable, Adaptive, and Resilient Autonomy (SARA)

University of Washington, 2020

Perception, planning, and control for navigation though complex, off-road terrain. I developed a control stack for enabling a Clearpath Warthog to navigate through a variety of complex, natural terrain, including mud, vegetation, and snow.

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Exploiting Singular Configurations for Controllable, Low-Power Friction Enhancement on Unmanned Ground Vehicles

Adam Foris, Nolan Wagener, Byron Boots, Anirban Mazumdar
IEEE Robotics and Automation Letters (RA-L), presented at ICRA, 2020
Paper  /  Video  /  Talk

A low-power wheel attachment that greatly increases traction on low-friction surfaces like synthetic ice.

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An Online Learning Approach to Model Predictive Control

Nolan Wagener*, Ching-An Cheng*, Jacob Sacks, Byron Boots
Robotics: Science and Systems (RSS), 2019
Winner of Best Student Paper Award
Finalist for Best Systems Paper Award

Paper  /  Video  /  Talk  /  Poster  /  Slides

A connection between model predictive control (MPC) and online learning, demonstrating that many well-known MPC algorithms are special cases of dynamic mirror descent.

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Fast Policy Learning Through Imitation and Reinforcement

Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots
Uncertainty in Artificial Intelligence (UAI), 2018
Plenary Presentation
Paper  /  Talk  /  Poster

A simple yet effective policy optimization algorithm that first performs imitation learning and then switches to reinforcement learning. Theory and experiments show that the policy learns nearly as fast as if performing reinforcement learning starting from the expert that we’re imitating.

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Information Theoretic MPC for Model-Based Reinforcement Learning

Grady Williams, Nolan Wagener, Brian Goldfain, Paul Drews, James Rehg, Byron Boots, Evangelos Theodorou
IEEE International Conference on Robotics and Automation (ICRA), 2017
Finalist for Best Conference Paper Award
Paper  /  Video  /  Code  /  Colab  /  Poster  /  Slides

MPPI, a sampling-based model predictive control algorithm, which can handle general nonlinear dynamics and discontinuous costs. We demonstrate MPPI’s capability on an aggressive driving task.

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Learning Contact-Rich Manipulation Skills with Guided Policy Search

Sergey Levine, Nolan Wagener, Pieter Abbeel
IEEE International Conference on Robotics and Automation (ICRA), 2015
Winner of Best Robotic Manipulation Paper Award
Paper  /  Article  /  Video  /  Talk

An application of guided policy search, a reinforcement learning algorithm which alternates between trajectory optimization and supervised learning, that allows a PR2 robot to efficiently learn manipulation skills.

Other Projects

These include unpublished research and coursework.

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Consistent Dropout for Policy Gradient Reinforcement Learning

Matthew Hausknecht, Nolan Wagener
arXiv, 2022

Identify a stability issue with naive application of dropout in policy gradient and propose a simple fix for it.

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Value Function Learning for AutoRally Racing

CS 8751: Robotics Research, Georgia Tech, 2019
Report  /  Poster

Establish a connection between maximum entropy reinforcement learning and model predictive control, specifically MPPI. Propose a value learning scheme based on MPPI, and demonstrate results on simulated AutoRally task.

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End-to-End Training of an Optimal Control Network

CS 7643: Deep Learning, Georgia Tech, 2017

Perform imitation learning with a policy based on MPPI.

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Empirical Evaluation of Recurrent Neural Networks for System Identification

CS 8803 STR: Statistical Techniques in Robotics, Georgia Tech, 2016
Report  /  Slides

Investigate training of recurrent neural networks for system identification, including pre-training techniques for identifying the (unobserved) state.

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The Printzerria

ME 102B: Mechatronics Design, UC Berkeley, 2014
Website  /  Video

A 3D printer for making pizza, with “extrusion” tubes for dough, tomato paste, and cheese. I programmed the robot.

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Line-Maze Solving Robot

EE 149: Embedded Systems, UC Berkeley, 2013
Report  /  Video  /  Slides

An m3pi robot that can navigate an unknown line maze, complete with doors and waypoints represented by colored tape. I programmed the maze navigation algorithm and the color detection from the color sensor.

Design and source code from Leonid Keselman's website, which itself is based on Jon Barron's website.