Shaping Couzin-Like Torus Swarms through Coordinated Mediation. - Shin-Young Jun; Daniel S. Brown; Michael A. Goodrich (SMC, 2013)

Limited bandwidth recognition of collective behaviors in bio-inspired swarms. - Daniel S. Brown; Michael A. Goodrich (AAMAS, 2014)

Human-swarm interactions based on managing attractors. - Daniel S. Brown; Sean C. Kerman; Michael A. Goodrich (HRI, 2014)

Balancing human and inter-agent influences for shared control of bio-inspired collectives. - Daniel S. Brown; Shin-Young Jun; Michael A. Goodrich (SMC, 2014)

k-Agent Sufficiency for Multiagent Stochastic Physical Search Problems. - Daniel S. Brown; Steven Loscalzo; Nathaniel Gemelli (ADT, 2015)

Multiobjective Optimization for the Stochastic Physical Search Problem. - Jeffrey Hudack; Nathaniel Gemelli; Daniel S. Brown; Steven Loscalzo; Jae C. Oh (IEA/AIE, 2015)

Discovery and Exploration of Novel Swarm Behaviors Given Limited Robot Capabilities. - Daniel S. Brown; Ryan Turner; Oliver Hennigh; Steven Loscalzo (DARS, 2016)

Classifying swarm behavior via compressive subspace learning. - Matthew Berger; Lee M. Seversky; Daniel S. Brown (ICRA, 2016)

Evolving and Controlling Perimeter, Rendezvous, and Foraging Behaviors in a Computation-Free Robot Swarm. - Matthew Johnson; Daniel S. Brown (EAI Endorsed Trans. Collab. Comput., 2016)

Two invariants of human-swarm interaction. - Daniel S. Brown; Michael A. Goodrich; Shin-Young Jung; Sean Kerman (J. Hum. Robot Interact., 2016)

Toward Probabilistic Safety Bounds for Robot Learning from Demonstration. - Daniel S. Brown; Scott Niekum (AAAI Fall Symposia, 2017)

Exact and Heuristic Algorithms for Risk-Aware Stochastic Physical Search. - Daniel S. Brown; Jeffrey Hudack; Nathaniel Gemelli; Bikramjit Banerjee (Comput. Intell., 2017)

Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. - Daniel S. Brown; Scott Niekum (AAAI, 2018)

Risk-Aware Active Inverse Reinforcement Learning. - Daniel S. Brown; Yuchen Cui; Scott Niekum (CoRL, 2018)

LAAIR: A Layered Architecture for Autonomous Interactive Robots. - Yuqian Jiang; Nick Walker 001; Minkyu Kim; Nicolas Brissonneau; Daniel S. Brown; Justin W. Hart; Scott Niekum; Luis Sentis; Peter Stone 001 (CoRR, 2018)

Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. - Daniel S. Brown; Scott Niekum (AAAI, 2019)

Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations. - Daniel S. Brown; Wonjoon Goo; Scott Niekum (CoRL, 2019)

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. - Daniel S. Brown; Wonjoon Goo; Prabhat Nagarajan; Scott Niekum (ICML, 2019)

Ranking-Based Reward Extrapolation without Rankings. - Daniel S. Brown; Wonjoon Goo; Scott Niekum (CoRR, 2019)

Deep Bayesian Reward Learning from Preferences. - Daniel S. Brown; Scott Niekum (CoRR, 2019)

Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. - Michael Danielczuk; Ashwin Balakrishna; Daniel S. Brown; Ken Goldberg (CoRL, 2020)

Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. - Daniel S. Brown; Russell Coleman; Ravi Srinivasan; Scott Niekum (ICML, 2020)

Bayesian Robust Optimization for Imitation Learning. - Daniel S. Brown; Scott Niekum; Marek Petrik (NeurIPS, 2020)

Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. - Michael Danielczuk; Ashwin Balakrishna; Daniel S. Brown; Shivin Devgon; Ken Goldberg (CoRR, 2020)

Value Alignment Verification. - Daniel S. Brown; Jordan Schneider; Scott Niekum (CoRR, 2020)

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 (CASE, 2021)

LazyDAgger: Reducing Context Switching in Interactive Imitation Learning. - Ryan Hoque; Ashwin Balakrishna; Carl Putterman; Michael Luo; Daniel S. Brown; Daniel Seita; Brijen Thananjeyan; Ellen R. Novoseller; Ken Goldberg (CASE, 2021)

ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning. - Ryan Hoque; Ashwin Balakrishna; Ellen R. Novoseller; Albert Wilcox; Daniel S. Brown; Ken Goldberg (CoRL, 2021)

Value Alignment Verification. - Daniel S. Brown; Jordan Schneider; Anca D. Dragan; Scott Niekum (ICML, 2021)

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 (ICML, 2021)

Dynamically Switching Human Prediction Models for Efficient Planning. - Arjun Sripathy; Andreea Bobu; Daniel S. Brown; Anca D. Dragan (ICRA, 2021)

Situational Confidence Assistance for Lifelong Shared Autonomy. - Matthew Zurek; Andreea Bobu; Daniel S. Brown; Anca D. Dragan (ICRA, 2021)

Optimal Cost Design for Model Predictive Control. - Avik Jain; Lawrence Chan; Daniel S. Brown; Anca D. Dragan (L4DC, 2021)

Offline Preference-Based Apprenticeship Learning. - Daniel Shin; Daniel S. Brown (CoRR, 2021)

Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies. - Satvik Sharma; Ellen R. Novoseller; Vainavi Viswanath; Zaynah Javed; Rishi Parikh; Ryan Hoque; Ashwin Balakrishna; Daniel S. Brown; Ken Goldberg (CASE, 2022)

Learning Representations that Enable Generalization in Assistive Tasks. - Jerry Zhi-Yang He; Zackory Erickson; Daniel S. Brown; Aditi Raghunathan; Anca D. Dragan (CoRL, 2022)

LEGS: Learning Efficient Grasp Sets for Exploratory Grasping. - Letian Fu; Michael Danielczuk; Ashwin Balakrishna; Daniel S. Brown; Jeffrey Ichnowski; Eugen Solowjow; Ken Goldberg (ICRA, 2022)

Teaching Robots to Span the Space of Functional Expressive Motion. - Arjun Sripathy; Andreea Bobu; Zhongyu Li 003; Koushil Sreenath; Daniel S. Brown; Anca D. Dragan (IROS, 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 (NeurIPS, 2022)

A Study of Causal Confusion in Preference-Based Reward Learning. - Jeremy Tien; Jerry Zhi-Yang He; Zackory Erickson; Anca D. Dragan; Daniel S. Brown (CoRR, 2022)

Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. - Tu Trinh; Daniel S. Brown (CoRR, 2022)

Bayesian Methods for Constraint Inference in Reinforcement Learning. - Dimitris Papadimitriou; Usman Anwar; Daniel S. Brown (Trans. Mach. Learn. Res., 2022)

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, 2023)

Player-Centric Procedural Content Generation: Enhancing Runtime Customization by Integrating Real-Time Player Feedback. - Nancy N. Blackburn; M. Gardone; Daniel S. Brown (CHI PLAY (Companion), 2023)

Quantifying Assistive Robustness Via the Natural-Adversarial Frontier. - Jerry Zhi-Yang He; Daniel S. Brown; Zackory Erickson; Anca D. Dragan (CoRL, 2023)

Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms. - Connor Mattson; Daniel S. Brown (GECCO, 2023)

SIRL: Similarity-based Implicit Representation Learning. - Andreea Bobu; Yi Liu; Rohin Shah; Daniel S. Brown; Anca D. Dragan (HRI, 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 (ICLR, 2023)

Contextual Reliability: When Different Features Matter in Different Contexts. - Gaurav Rohit Ghosal; Amrith Setlur; Daniel S. Brown; Anca D. Dragan; Aditi Raghunathan (ICML, 2023)

Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models. - Yi Liu; Gaurav Datta; Ellen R. Novoseller; Daniel S. Brown (ICRA, 2023)

Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots. - Connor Mattson; Jeremy C. Clark; Daniel S. Brown (MRS, 2023)

Can Differentiable Decision Trees Learn Interpretable Reward Functions? - Akansha Kalra; Daniel S. Brown (CoRR, 2023)

Swarm Mechanics and Swarm Chemistry: A Transdisciplinary Approach for Robot Swarms. - Ricardo Vega; Kevin Zhu; Connor Mattson; Daniel S. Brown; Cameron Nowzari (CoRR, 2023)

Benchmarks and Algorithms for Offline Preference-Based Reward Learning. - Daniel Shin; Anca D. Dragan; Daniel S. Brown (Trans. Mach. Learn. Res., 2023)

Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. - Tu Trinh; Haoyu Chen; Daniel S. Brown (HRI, 2024)

Spiking Neural Networks as a Controller for Emergent Swarm Agents. - Kevin Zhu; Connor Mattson; Shay Snyder; Ricardo Vega; Daniel S. Brown; Maryam Parsa; Cameron Nowzari (ICONS, 2024)

Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models. - Dimitris Papadimitriou; Daniel S. Brown (ICRA, 2024)

Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery. - Zohre Karimi; Shing-Hei Ho; Bao Thach; Alan Kuntz; Daniel S. Brown (ISMR, 2024)

Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks. - Jordan Thompson; Brian Y. Cho; Daniel S. Brown; Alan Kuntz (ISMR, 2024)

ROIL: Robust Offline Imitation Learning without Trajectories. - Gersi Doko; Guang Yang; Daniel S. Brown; Marek Petrik (RLJ, 2024)

Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback? - Akansha Kalra; Daniel S. Brown (RLJ, 2024)

Representation Alignment from Human Feedback for Cross-Embodiment Reward Learning from Mixed-Quality Demonstrations. - Connor Mattson; Anurag Aribandi; Daniel S. Brown (RLJ, 2024)

Agent-Based Emulation for Deploying Robot Swarm Behaviors. - Ricardo Vega; Kevin Zhu; Connor Mattson; Daniel S. Brown; Cameron Nowzari (CoRR, 2024)

Leveraging Human Input to Enable Robust, Interactive, and Aligned AI Systems. - Daniel S. Brown (AAAI, 2025)

Toward Zero-Shot User Intent Recognition in Shared Autonomy. - Atharv Belsare; Zohre Karimi; Connor Mattson; Daniel S. Brown (HRI, 2025)

Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer. - Connor Mattson; Varun Raveendra; Ricardo Vega; Cameron Nowzari; Daniel S. Drew; Daniel S. Brown (AAMAS, 2025)

Uncertainty Aware Forward and Inverse Kinematics for Tendon-Driven Continuum Robots via Mixture Density Networks. - Jordan Thompson; Brian Y. Cho; Daniel S. Brown; Alan Kuntz (J. Medical Robotics Res., 2025)

Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation via Uncertainty Quantification. - Jordan Thompson; Ronald Koe; Anthony Le; Gabriella Goodman; Daniel S. Brown; Alan Kuntz (CoRR, 2025)

How vulnerable is my policy? Adversarial attacks on modern behavior cloning policies. - Basavasagar Patil; Akansha Kalra; Guanhong Tao 001; Daniel S. Brown (CoRR, 2025)