| 2026 | ACL | RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions. | Drishti Goel, Jeongah Lee, Qiuyue Joy Zhong, Violeta J. Rodriguez, Daniel S. Brown, Ravi Karkar, Dong Whi Yoo, Koustuv Saha |
| 2025 | AAAI | Leveraging Human Input to Enable Robust, Interactive, and Aligned AI Systems. | Daniel S. Brown |
| 2025 | HRI | Toward Zero-Shot User Intent Recognition in Shared Autonomy. | Atharv Belsare, Zohre Karimi, Connor Mattson, Daniel S. Brown |
| 2024 | HRI | Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. | Tu Trinh, Haoyu Chen, Daniel S. Brown |
| 2024 | ICRA | Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models. | Dimitris Papadimitriou, Daniel S. Brown |
| 2023 | AAAI | 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 |
| 2023 | CoRL | Quantifying Assistive Robustness Via the Natural-Adversarial Frontier. | Jerry Zhi-Yang He, Daniel S. Brown, Zackory Erickson, Anca D. Dragan |
| 2023 | GECCO | Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms. | Connor Mattson, Daniel S. Brown |
| 2023 | HRI | SIRL: Similarity-based Implicit Representation Learning. | Andreea Bobu, Yi Liu, Rohin Shah, Daniel S. Brown, Anca D. Dragan |
| 2023 | ICLR | Causal Confusion and Reward Misidentification in Preference-Based Reward Learning. | Jeremy Tien, Jerry Zhi-Yang He, Zackory Erickson, Anca D. Dragan, Daniel S. Brown |
| 2023 | ICML | Contextual Reliability: When Different Features Matter in Different Contexts. | Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan |
| 2023 | ICRA | Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models. | Yi Liu, Gaurav Datta, Ellen R. Novoseller, Daniel S. Brown |
| 2022 | CoRL | Learning Representations that Enable Generalization in Assistive Tasks. | Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca D. Dragan |
| 2022 | ICRA | LEGS: Learning Efficient Grasp Sets for Exploratory Grasping. | Letian Fu, Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Jeffrey Ichnowski, Eugen Solowjow, Ken Goldberg |
| 2022 | IROS | Teaching Robots to Span the Space of Functional Expressive Motion. | Arjun Sripathy, Andreea Bobu, Zhongyu Li, Koushil Sreenath, Daniel S. Brown, Anca D. Dragan |
| 2021 | CoRL | 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 |
| 2021 | ICML | Value Alignment Verification. | Daniel S. Brown, Jordan Schneider, Anca D. Dragan, Scott Niekum |
| 2021 | ICML | 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 |
| 2021 | ICRA | Dynamically Switching Human Prediction Models for Efficient Planning. | Arjun Sripathy, Andreea Bobu, Daniel S. Brown, Anca D. Dragan |
| 2021 | ICRA | Situational Confidence Assistance for Lifelong Shared Autonomy. | Matthew Zurek, Andreea Bobu, Daniel S. Brown, Anca D. Dragan |
| 2020 | CoRL | Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. | Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Ken Goldberg |
| 2020 | ICML | Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. | Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum |
| 2019 | AAAI | Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. | Daniel S. Brown, Scott Niekum |
| 2019 | CoRL | Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations. | Daniel S. Brown, Wonjoon Goo, Scott Niekum |
| 2019 | ICML | Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. | Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum |
| 2018 | AAAI | Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. | Daniel S. Brown, Scott Niekum |
| 2018 | CoRL | Risk-Aware Active Inverse Reinforcement Learning. | Daniel S. Brown, Yuchen Cui, Scott Niekum |
| 2016 | ICRA | Classifying swarm behavior via compressive subspace learning. | Matthew Berger, Lee M. Seversky, Daniel S. Brown |
| 2014 | HRI | Human-swarm interactions based on managing attractors. | Daniel S. Brown, Sean C. Kerman, Michael A. Goodrich |
| 2014 | SMC | Balancing human and inter-agent influences for shared control of bio-inspired collectives. | Daniel S. Brown, Shin-Young Jun, Michael A. Goodrich |
| 2013 | SMC | Shaping Couzin-Like Torus Swarms through Coordinated Mediation. | Shin-Young Jun, Daniel S. Brown, Michael A. Goodrich |