Publications

Google Scholar Page

2024

Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery.
Zohre Karimi, Shing-Hei Ho, Bao Thach, Alan Kuntz, Daniel S. Brown
International Symposium on Medical Robotics (ISMR), 2024.
[Project Page]
Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks.
Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz
International Symposium on Medical Robotics (ISMR), 2024.
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning.
Tu Trinh, Haoyu Chen, Daniel S. Brown
ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2024. *Best Paper Award Finalist*
Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models.
Dimitris Papadimitriou, Daniel S. Brown
IEEE International Conference on Robotics and Automation (ICRA), 2024.
[Project Page]

2023

Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots.
Connor Mattson, Jeremy C. Clark, Daniel S. Brown
International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2023.
[Project Page]
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier.
Jerry Zhi-Yang He, Daniel S Brown, Zackory Erickson, Anca Dragan
Conference on Robot Learning (CoRL), 2023.
[Project Page]
Player-Centric Procedural Content Generation: Enhancing Runtime Customization by Integrating Real-Time Player Feedback.
Nancy N Blackburn, M Gardone, Daniel S Brown
Companion Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY), 2023.
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models.
Yi Liu, Gaurav Datta, Ellen Novoseller, Daniel S. Brown
International Conference on Robotics and Automation (ICRA), 2023.
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms.
Connor Mattson, Daniel S. Brown
Genetic and Evolutionary Computation Conference (GECCO), 2023.
[Project Page]
Contextual Reliability: When Different Features Matter in Different Contexts.
Gaurav Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
International Conference on Machine Learning (ICML), 2023.
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning.
Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca D. Dragan, Daniel S. Brown
International Conference on Learning Representations (ICLR), 2023.
Benchmarks and Algorithms for Offline Preference-Based Reward Learning.
Daniel Shin, Anca D. Dragan, Daniel S. Brown
Transactions on Machine Learning Research (TMLR), 2023.
SIRL: Similarity-based Implicit Representation Learning.
Andreea Bobu, Yi Liu, Rohin Shah, Daniel S. Brown, Anca D. Dragan
International Conference on Human Robot Interaction (HRI), 2023.
Towards a Gaze-Driven Assistive Neck Exoskeleton via Virtual Reality Data Collection.
Jordan Thompson, Haohan Zhang, Daniel S. Brown
HRI Workshop on Virtual, Augmented, and Mixed-Reality for Human-Robot Interactions, 2023.
The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types.
Gaurav R. Ghosal, Matthew Zurek, Daniel S. Brown, Anca D. Dragan
AAAI Conference on Artificial Intelligence (AAAI), 2023.
Benchmarks and Algorithms for Offline Preference-Based Reward Learning.
Daniel Shin, Anca D Dragan, Daniel S Brown
Transactions on Machine Learning Research (TMLR), 2023.

2022

Interpretable Reward Learning via Differentiable Decision Trees..
Akansha Kalra, Daniel S. Brown
NeurIPS Workshop on ML Safety, 2022.
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning.
Tu Trinh, Daniel S. Brown
AAAI FSS-22 Symposium on Lessons Learned for Autonomous Assessment of Machine Abilities, 2022.
Learning Representations that Enable Generalization in Assistive Tasks.
Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca Dragan
Conference on Robot Learning (CoRL), 2022.
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations.
Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane, Daniel S. Brown, Ken Goldberg
Neural Information Processing Systems (NeurIPS), 2022.
[Project Page]
Teaching Robots to Span the Space of Functional Expressive Motion.
Arjun Sripathy, Andreea Bobu, Zhongyu Li, Koushil Sreenath, Daniel S. Brown, Anca D. Dragan
International Conference on Robot and Systems (IROS), 2022.
[Project Page]
Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies.
Satvik Sharma, Ellen Novoseller, Vainavi Viswanath, Zaynah Javed, Rishi Parikh, Ryan Hoque, Ashwin Balakrishna, Daniel S. Brown, Ken Goldberg
International Conference on Automation Science and Engineering (CASE), 2022.
[Project Page]
A Study of Causal Confusion in Preference-Based Reward Learning.
Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca D. Dragan, Daniel S. Brown
RSS Workshop on Overlooked Aspects of Imitation Learning: Systems, Data, Tasks, and Beyond, 2022.
[Project Page]
LEGS: Learning Efficient Grasp Sets for Exploratory Grasping.
Letian Fu, Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Jeffrey Ichnowski, Eugen Solowjow, Ken Goldberg
International Conference on Robotics and Automation (ICRA), 2022.
[Project Page]

2021

Unbiased Efficient Feature Counts for Inverse RL..
Gerard Donahue, Brendan Crowe, Marek Petrik, Daniel S. Brown
NeurIPS Workshop on Safe and Robust Control of Uncertain Systems, 2021.
Bayesian Inverse Constrained Reinforcement Learning.
Dimitris Papadimitriou, Usman Anwar, Daniel S. Brown
NeurIPS Workshop on Safe and Robust Control of Uncertain Systems, 2021.
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning.
Ryan Hoque, Ashwin Balakrishna, Ellen Novoseller, Albert Wilcox, Daniel S. Brown, Ken Goldberg
Conference on Robot Learning (CoRL), 2021.
[Project Page]
Offline Preference-Based Apprenticeship Learning.
Daniel Shin, Daniel S. Brown
ICML Workshop on Human-AI Collaboration in Sequential Decision-Making, 2021.
[Project page]
Value Alignment Verification.
Daniel S. Brown*, Jordan Schneider*, Anca D. Dragan, Scott Niekum
International Conference on Machine Learning (ICML), 2021.
[Project page]
Policy Gradient Bayesian Robust Optimization for Imitation Learning.
Zaynah Javed*, Daniel S. Brown*, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg.
International Conference on Machine Learning (ICML), 2021.
[Project page]
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning.
Ryan Hoque, Ashwin Balakrishna, Carl Putterman, Michael Luo, Daniel S. Brown, Daniel Seita, Brijen Thananjeyan, Ellen Novoseller, Ken Goldberg.
IEEE Conference on Automation Science and Engineering (CASE), 2021.
[Project page]
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities.
Shivin Devgon, Jeffrey Ichnowski, Michael Danielczuk, Daniel S. Brown, Ashwin Balakrishna, Shirin Joshi, Eduardo M. C. Rocha, Eugen Solowjow, Ken Goldberg
IEEE Conference on Automation Science and Engineering (CASE), 2021.
[Video] [Slides] [Code]
Optimal Cost Design for Model Predictive Control.
Avik Jain, Lawrence Chan, Daniel S. Brown, Anca D. Dragan.
3rd Annual Learning for Dynamics & Control Conference (L4DC), 2021.
[Project page]
Dynamically Switching Human Prediction Models for Efficient Planning.
Arjun Sripathy, Andreea Bobu, Daniel S. Brown, Anca D. Dragan.
International Conference on Robotics and Automation (ICRA), 2021.
[Project page]
Situational Confidence Assistance for Lifelong Shared Autonomy.
Matthew Zurek, Andreea Bobu, Daniel S. Brown, Anca D. Dragan.
International Conference on Robotics and Automation (ICRA), 2021.

2020

Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects.
Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Shivin Devgon, Ken Goldberg
Conference on Robot Learning (CoRL), 2020.
[Project page] [Video]
Bayesian Robust Optimization for Imitation Learning.
Daniel S. Brown, Scott Niekum, Marek Petrik
Neural Information Processing Systems (NeurIPS), 2020.
[Video] [Code]
Safe and Efficient Inverse Reinforcement Learning.
Daniel S. Brown
Doctoral Dissertation, Department of Computer Science, University of Texas at Austin, 2020.
[Video]
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences.
Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum.
International Conference on Machine Learning (ICML), 2020.
[Project page] [Video] [Code]

2019

Deep Bayesian Reward Learning from Preferences.
Daniel S. Brown, Scott Niekum.
NeurIPS Workshop on Safety and Robustness in Decision Making, 2019.
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations.
Daniel S. Brown, Wonjoon Goo, Scott Niekum.
Conference on Robot Learning (CoRL), 2019.
[Project page] [Video] [Code]
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations.
Daniel S. Brown*, Wonjoon Goo*, Prabhat Nagarajan, Scott Niekum.
International Conference on Machine Learning (ICML), 2019.
[Project page] [Video] [Code]
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications.
Daniel S. Brown, Scott Niekum.
AAAI Conference on Artificial Intelligence, 2019.
[Code]

2018

Risk-Aware Active Inverse Reinforcement Learning.
Daniel S. Brown*, Yuchen Cui*, Scott Niekum
Conference on Robot Learning (CoRL), 2018.
[Video] [Code]
LAAIR: A Layered Architecture for Autonomous Interactive Robots.
Yuchian Jian, Nick Walker, Minkyu Kim, Nicolas Brissonneau, Daniel S. Brown, Justin W. Hart, Scott Niekum, Luis Sentis, Peter Stone
AAAI Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy, 2018.
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning.
Daniel S. Brown, Scott Niekum.
AAAI Conference on Artificial Intelligence, 2018.
[PowerPoint] [Pdf Slides] [Code]

2017

Toward Probabilistic Safety Bounds for Robot Learning from Demonstration.
Daniel S. Brown, Scott Niekum.
AAAI Fall Symposium on Artificial Intelligence for Human-Robot Interaction, 2017.

2016

Discovery and Exploration of Novel Swarm Behaviors given Limited Robot Capabilities.
Daniel S. Brown, Ryan Turner, Oliver Hennigh, Steven Loscalzo.
International Symposium on Distributed Autonomous Robotic Systems, 2016. *Best Paper Award Finalist*
Two Invariants of Human-Swarm Interaction.
Daniel S. Brown, Michael A. Goodich, Shin-Young Jung, and Sean Kerman.
Journal of Human-Robot Interaction, 5(1), 2016, pp. 1-31.
[Code]
Exact and Heuristic Algorithms for Risk-Aware Stochastic Physical Search.
Daniel S. Brown, Jeffrey Hudack, Nathaniel Gemelli, Bikramjit Banerjee.
Computational Intelligence, 2016.
Classifying Swarm Behaviors via Compressive Subspace Learning.
Matthew Berger, Lee M. Seversky, Daniel S. Brown.
International Conference on Robotics and Automation, 2016.

2015

Evolving and Controlling Perimeter, Rendezvous, and Foraging Behaviors in a Computation-Free Robot Swarm.
Matthew Johnson, Daniel S. Brown.
International Conference on Bio-inspired Information and Communications Technologies, 2015.
k-Agent Sufficiency for Multiagent Stochastic Physical Search Problems.
Daniel S. Brown, Steven Loscalzo, Nathaniel Gemelli.
International Conference on Algorithmic Decision Theory, 2015.
Multiobjective Optimization for the Stochastic Physical Search Problem.
Jeffrey Hudack, Nathaniel Gemelli, Daniel S. Brown, Steven Loscalzo, Jae C. Oh.
International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2015.
Algorithms for Stochastic Physical Search on General Graphs.
Daniel S. Brown, Jeffrey Hudack, Bikramjit Banerjee.
Planning, Search, and Optimization Workshop at the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.

2014

Balancing human and inter-agent influences for shared control of bio-inspired collectives.
Daniel S. Brown, Shin-Young. Jung, and Michael A. Goodrich.
Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. , 2014.
Limited Bandwidth Recognition of Collective Behaviors in Bio-Inspired Swarms.
Daniel S. Brown and Michael A. Goodrich.
Autonomous Agents and Multiagent Systems (AAMAS), 2014.
Human-Swarm Interactions Based on Managing Attractors.
Daniel S. Brown, Sean Kerman, and Michael A. Goodrich.
In International Conference on Human-Robot Interaction (HRI), 2014. *Best Paper Award Finalist*
[Code]

2013

Toward Scalable Human-Swarm Interaction with Bio-Inspired Robot Teams.
Daniel S. Brown.
Masters Thesis, Brigham Young University, March 2013.
Shaping Couzin-like Torus Swarms through Coordinated Mediation.
Shin-Young Jung, Daniel S. Brown, and Michael A. Goodrich.
Proceedings of the 2013 International Conference on Systems, Man, and Cybernetics,2013.

2012

Supporting Human Interaction with Robust Robot Swarms.
Sean Kerman, Daniel S. Brown, and Michael A. Goodrich.
Proceedings of the International Symposium on Resilient Control Systems, 2012.
[Code]

2011

Learning and Control Techniques for Portfolio Optimization.
Daniel S. Brown.
Honors Thesis, Brigham Young University, 2011.