Daniel S. Brown

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 80

Top Topics

Robust Optimization

Reinforcement Learning

Polyhedral Objects

Collective Behavior

Top Venues

Publications

Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz
Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks. CoRR (2024)
Tu Trinh, Haoyu Chen, Daniel S. Brown
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. HRI (2024)
Dimitris Papadimitriou, Daniel S. Brown
Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models. CoRR (2024)
Zohre Karimi, Shing-Hei Ho, Bao Thach, Alan Kuntz, Daniel S. Brown
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery. CoRR (2024)
Dimitris Papadimitriou, Daniel S. Brown
Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models. ICRA (2024)
Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz
Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks. ISMR (2024)
Zohre Karimi, Shing-Hei Ho, Bao Thach, Alan Kuntz, Daniel S. Brown
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery. ISMR (2024)
Daniel Shin, Anca D. Dragan, Daniel S. Brown
Benchmarks and Algorithms for Offline Preference-Based Reward Learning. CoRR (2023)
Gaurav R. Ghosal, Matthew Zurek, Daniel S. Brown, Anca D. Dragan
The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types. AAAI (2023)
Yi Liu, Gaurav Datta, Ellen R. Novoseller, Daniel S. Brown
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models. ICRA (2023)
Andreea Bobu, Yi Liu, Rohin Shah, Daniel S. Brown, Anca D. Dragan
SIRL: Similarity-based Implicit Representation Learning. CoRR (2023)
Connor Mattson, Jeremy C. Clark, Daniel S. Brown
Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots. MRS (2023)
Yi Liu, Gaurav Datta, Ellen R. Novoseller, Daniel S. Brown
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models. CoRR (2023)
Jerry Zhi-Yang He, Daniel S. Brown, Zackory Erickson, Anca D. Dragan
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier. CoRL (2023)
Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
Contextual Reliability: When Different Features Matter in Different Contexts. ICML (2023)
Daniel Shin, Anca D. Dragan, Daniel S. Brown
Benchmarks and Algorithms for Offline Preference-Based Reward Learning. Trans. Mach. Learn. Res. 2023 (2023)
Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca D. Dragan, Daniel S. Brown
Causal Confusion and Reward Misidentification in Preference-Based Reward Learning. ICLR (2023)
Andreea Bobu, Yi Liu, Rohin Shah, Daniel S. Brown, Anca D. Dragan
SIRL: Similarity-based Implicit Representation Learning. HRI (2023)
Connor Mattson, Daniel S. Brown
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms. GECCO (2023)
Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Anca D. Dragan
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier. CoRR (2023)
Gaurav R. Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan
Contextual Reliability: When Different Features Matter in Different Contexts. CoRR (2023)
Nancy N. Blackburn, M. Gardone, Daniel S. Brown
Player-Centric Procedural Content Generation: Enhancing Runtime Customization by Integrating Real-Time Player Feedback. CHI PLAY Companion (2023)
Ricardo Vega, Kevin Zhu, Connor Mattson, Daniel S. Brown, Cameron Nowzari
Swarm Mechanics and Swarm Chemistry: A Transdisciplinary Approach for Robot Swarms. CoRR (2023)
Connor Mattson, Daniel S. Brown
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms. CoRR (2023)
Akansha Kalra, Daniel S. Brown
Can Differentiable Decision Trees Learn Interpretable Reward Functions? CoRR (2023)
Connor Mattson, Jeremy C. Clark, Daniel S. Brown
Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots. CoRR (2023)
Letian Fu, Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Jeffrey Ichnowski, Eugen Solowjow, Ken Goldberg
LEGS: Learning Efficient Grasp Sets for Exploratory Grasping. ICRA (2022)
Satvik Sharma, Ellen R. Novoseller, Vainavi Viswanath, Zaynah Javed, Rishi Parikh, Ryan Hoque, Ashwin Balakrishna, Daniel S. Brown, Ken Goldberg
Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies. CASE (2022)
Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane, Daniel S. Brown, Ken Goldberg
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations. CoRR (2022)
Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown, Zackory Erickson, Anca D. Dragan
Learning Representations that Enable Generalization in Assistive Tasks. CoRR (2022)
Arjun Sripathy, Andreea Bobu, Zhongyu Li, Koushil Sreenath, Daniel S. Brown, Anca D. Dragan
Teaching Robots to Span the Space of Functional Expressive Motion. IROS (2022)
Dimitris Papadimitriou, Usman Anwar, Daniel S. Brown
Bayesian Methods for Constraint Inference in Reinforcement Learning. Trans. Mach. Learn. Res. 2022 (2022)
Gaurav R. Ghosal, Matthew Zurek, Daniel S. Brown, Anca D. Dragan
The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types. CoRR (2022)
Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane, Daniel S. Brown, Ken Goldberg
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations. NeurIPS (2022)
Satvik Sharma, Ellen R. Novoseller, Vainavi Viswanath, Zaynah Javed, Rishi Parikh, Ryan Hoque, Ashwin Balakrishna, Daniel S. Brown, Ken Goldberg
Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies. CoRR (2022)
Tu Trinh, Daniel S. Brown
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. CoRR (2022)
Arjun Sripathy, Andreea Bobu, Zhongyu Li, Koushil Sreenath, Daniel S. Brown, Anca D. Dragan
Teaching Robots to Span the Space of Functional Expressive Motion. CoRR (2022)
Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca D. Dragan, Daniel S. Brown
A Study of Causal Confusion in Preference-Based Reward Learning. CoRR (2022)
Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca D. Dragan
Learning Representations that Enable Generalization in Assistive Tasks. CoRL (2022)
Shivin Devgon, Jeffrey Ichnowski, Michael Danielczuk, Daniel S. Brown, Ashwin Balakrishna, Shirin Joshi, Eduardo M. C. Rocha, Eugen Solowjow, Ken Goldberg
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities. CoRR (2021)
Daniel Shin, Daniel S. Brown
Offline Preference-Based Apprenticeship Learning. CoRR (2021)
Zaynah Javed, Daniel S. Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg
Policy Gradient Bayesian Robust Optimization for Imitation Learning. CoRR (2021)
Shivin Devgon, Jeffrey Ichnowski, Michael Danielczuk, Daniel S. Brown, Ashwin Balakrishna, Shirin Joshi, Eduardo M. C. Rocha, Eugen Solowjow, Ken Goldberg
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities. CASE (2021)
Arjun Sripathy, Andreea Bobu, Daniel S. Brown, Anca D. Dragan
Dynamically Switching Human Prediction Models for Efficient Planning. ICRA (2021)
Daniel S. Brown, Jordan Schneider, Anca D. Dragan, Scott Niekum
Value Alignment Verification. ICML (2021)
Letian Fu, Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Jeffrey Ichnowski, Eugen Solowjow, Ken Goldberg
LEGS: Learning Efficient Grasp Sets for Exploratory Grasping. CoRR (2021)
Zaynah Javed, Daniel S. Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg
Policy Gradient Bayesian Robust Optimization for Imitation Learning. ICML (2021)
Avik Jain, Lawrence Chan, Daniel S. Brown, Anca D. Dragan
Optimal Cost Design for Model Predictive Control. L4DC (2021)
Ryan Hoque, Ashwin Balakrishna, Ellen R. Novoseller, Albert Wilcox, Daniel S. Brown, Ken Goldberg
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning. CoRR (2021)
Matthew Zurek, Andreea Bobu, Daniel S. Brown, Anca D. Dragan
Situational Confidence Assistance for Lifelong Shared Autonomy. CoRR (2021)
Ryan Hoque, Ashwin Balakrishna, Ellen R. Novoseller, Albert Wilcox, Daniel S. Brown, Ken Goldberg
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning. CoRL (2021)
Avik Jain, Lawrence Chan, Daniel S. Brown, Anca D. Dragan
Optimal Cost Design for Model Predictive Control. CoRR (2021)
Ryan Hoque, Ashwin Balakrishna, Carl Putterman, Michael Luo, Daniel S. Brown, Daniel Seita, Brijen Thananjeyan, Ellen R. Novoseller, Ken Goldberg
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning. CASE (2021)
Matthew Zurek, Andreea Bobu, Daniel S. Brown, Anca D. Dragan
Situational Confidence Assistance for Lifelong Shared Autonomy. ICRA (2021)
Arjun Sripathy, Andreea Bobu, Daniel S. Brown, Anca D. Dragan
Dynamically Switching Human Prediction Models for Efficient Planning. CoRR (2021)
Ryan Hoque, Ashwin Balakrishna, Carl Putterman, Michael Luo, Daniel S. Brown, Daniel Seita, Brijen Thananjeyan, Ellen R. Novoseller, Ken Goldberg
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning. CoRR (2021)
Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. ICML (2020)
Daniel S. Brown, Jordan Schneider, Scott Niekum
Value Alignment Verification. CoRR (2020)
Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Shivin Devgon, Ken Goldberg
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. CoRR (2020)
Daniel S. Brown, Scott Niekum, Marek Petrik
Bayesian Robust Optimization for Imitation Learning. CoRR (2020)
Daniel S. Brown, Scott Niekum, Marek Petrik
Bayesian Robust Optimization for Imitation Learning. NeurIPS (2020)
Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Ken Goldberg
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. CoRL (2020)
Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. CoRR (2020)
Daniel S. Brown, Scott Niekum
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. AAAI (2019)
Daniel S. Brown, Yuchen Cui, Scott Niekum
Risk-Aware Active Inverse Reinforcement Learning. CoRR (2019)
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. ICML (2019)
Daniel S. Brown, Wonjoon Goo, Scott Niekum
Ranking-Based Reward Extrapolation without Rankings. CoRR (2019)
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. CoRR (2019)
Daniel S. Brown, Scott Niekum
Deep Bayesian Reward Learning from Preferences. CoRR (2019)
Daniel S. Brown, Wonjoon Goo, Scott Niekum
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations. CoRL (2019)
Daniel S. Brown, Yuchen Cui, Scott Niekum
Risk-Aware Active Inverse Reinforcement Learning. CoRL (2018)
Daniel S. Brown, Scott Niekum
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. AAAI (2018)
Yuqian Jiang, Nick Walker, Minkyu Kim, Nicolas Brissonneau, Daniel S. Brown, Justin W. Hart, Scott Niekum, Luis Sentis, Peter Stone
LAAIR: A Layered Architecture for Autonomous Interactive Robots. CoRR (2018)
Daniel S. Brown, Scott Niekum
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. CoRR (2018)
Daniel S. Brown, Scott Niekum
Toward Probabilistic Safety Bounds for Robot Learning from Demonstration. AAAI Fall Symposia (2017)
Daniel S. Brown, Jeffrey Hudack, Nathaniel Gemelli, Bikramjit Banerjee
Exact and Heuristic Algorithms for Risk-Aware Stochastic Physical Search. Comput. Intell. 33 (3) (2017)
Daniel S. Brown, Scott Niekum
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. CoRR (2017)
Matthew Berger, Lee M. Seversky, Daniel S. Brown
Classifying swarm behavior via compressive subspace learning. ICRA (2016)
Daniel S. Brown, Ryan Turner, Oliver Hennigh, Steven Loscalzo
Discovery and Exploration of Novel Swarm Behaviors Given Limited Robot Capabilities. DARS (2016)
Daniel S. Brown, Michael A. Goodrich, Shin-Young Jung, Sean Kerman
Two invariants of human-swarm interaction. J. Hum. Robot Interact. 5 (1) (2016)
Daniel S. Brown, Steven Loscalzo, Nathaniel Gemelli
k-Agent Sufficiency for Multiagent Stochastic Physical Search Problems. ADT (2015)
Jeffrey Hudack, Nathaniel Gemelli, Daniel S. Brown, Steven Loscalzo, Jae C. Oh
Multiobjective Optimization for the Stochastic Physical Search Problem. IEA/AIE (2015)
Daniel S. Brown, Sean C. Kerman, Michael A. Goodrich
Human-swarm interactions based on managing attractors. HRI (2014)
Daniel S. Brown, Michael A. Goodrich
Limited bandwidth recognition of collective behaviors in bio-inspired swarms. AAMAS (2014)
Daniel S. Brown, Shin-Young Jun, Michael A. Goodrich
Balancing human and inter-agent influences for shared control of bio-inspired collectives. SMC (2014)
Shin-Young Jun, Daniel S. Brown, Michael A. Goodrich
Shaping Couzin-Like Torus Swarms through Coordinated Mediation. SMC (2013)