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2020 – today
- 2024
[c43]Tu Trinh
, Haoyu Chen, Daniel S. Brown:
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. HRI 2024: 725-733- [c42]Dimitris Papadimitriou, Daniel S. Brown:
Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models. ICRA 2024: 15039-15046 - [c41]Zohre Karimi, Shing-Hei Ho, Bao Thach, Alan Kuntz, Daniel S. Brown:
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery. ISMR 2024: 1-7 - [c40]Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz:
Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks. ISMR 2024: 1-7 - [i40]Dimitris Papadimitriou, Daniel S. Brown:
Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models. CoRR abs/2403.02431 (2024) - [i39]Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz:
Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks. CoRR abs/2404.04241 (2024) - [i38]Zohre Karimi, Shing-Hei Ho, Bao Thach, Alan Kuntz, Daniel S. Brown:
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery. CoRR abs/2404.07185 (2024) - [i37]Connor Mattson, Anurag Aribandi, Daniel S. Brown:
Representation Alignment from Human Feedback for Cross-Embodiment Reward Learning from Mixed-Quality Demonstrations. CoRR abs/2408.05610 (2024) - 2023
- [j4]Daniel Shin, Anca D. Dragan, Daniel S. Brown:
Benchmarks and Algorithms for Offline Preference-Based Reward Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c39]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: 5983-5992 - [c38]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: 10-16 - [c37]Jerry Zhi-Yang He, Daniel S. Brown, Zackory Erickson, Anca D. Dragan:
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier. CoRL 2023: 1865-1886 - [c36]Connor Mattson, Daniel S. Brown:
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms. GECCO 2023: 56-64 - [c35]Andreea Bobu, Yi Liu, Rohin Shah, Daniel S. Brown, Anca D. Dragan:
SIRL: Similarity-based Implicit Representation Learning. HRI 2023: 565-574 - [c34]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 - [c33]Gaurav Rohit Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan:
Contextual Reliability: When Different Features Matter in Different Contexts. ICML 2023: 11300-11320 - [c32]Yi Liu, Gaurav Datta, Ellen R. Novoseller, Daniel S. Brown:
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models. ICRA 2023: 2921-2928 - [c31]Connor Mattson, Jeremy C. Clark, Daniel S. Brown:
Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots. MRS 2023: 163-169 - [i36]Andreea Bobu, Yi Liu, Rohin Shah, Daniel S. Brown, Anca D. Dragan:
SIRL: Similarity-based Implicit Representation Learning. CoRR abs/2301.00810 (2023) - [i35]Daniel Shin, Anca D. Dragan, Daniel S. Brown:
Benchmarks and Algorithms for Offline Preference-Based Reward Learning. CoRR abs/2301.01392 (2023) - [i34]Yi Liu, Gaurav Datta, Ellen R. Novoseller, Daniel S. Brown:
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models. CoRR abs/2301.04741 (2023) - [i33]Connor Mattson, Daniel S. Brown:
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms. CoRR abs/2305.16148 (2023) - [i32]Akansha Kalra, Daniel S. Brown:
Can Differentiable Decision Trees Learn Interpretable Reward Functions? CoRR abs/2306.13004 (2023) - [i31]Gaurav R. Ghosal, Amrith Setlur, Daniel S. Brown, Anca D. Dragan, Aditi Raghunathan:
Contextual Reliability: When Different Features Matter in Different Contexts. CoRR abs/2307.10026 (2023) - [i30]Ricardo Vega, Kevin Zhu, Connor Mattson, Daniel S. Brown, Cameron Nowzari:
Swarm Mechanics and Swarm Chemistry: A Transdisciplinary Approach for Robot Swarms. CoRR abs/2309.11408 (2023) - [i29]Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Anca D. Dragan:
Quantifying Assistive Robustness Via the Natural-Adversarial Frontier. CoRR abs/2310.10610 (2023) - [i28]Connor Mattson, Jeremy C. Clark, Daniel S. Brown:
Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots. CoRR abs/2310.16941 (2023) - 2022
- [j3]Dimitris Papadimitriou, Usman Anwar, Daniel S. Brown:
Bayesian Methods for Constraint Inference in Reinforcement Learning. Trans. Mach. Learn. Res. 2022 (2022) - [c30]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: 1116-1123 - [c29]Jerry Zhi-Yang He, Zackory Erickson, Daniel S. Brown, Aditi Raghunathan, Anca D. Dragan:
Learning Representations that Enable Generalization in Assistive Tasks. CoRL 2022: 2105-2114 - [c28]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: 8259-8265 - [c27]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: 13406-13413 - [c26]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 - [i27]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 abs/2203.02091 (2022) - [i26]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 abs/2204.06601 (2022) - [i25]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 abs/2207.00911 (2022) - [i24]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 abs/2208.10687 (2022) - [i23]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 abs/2210.07432 (2022) - [i22]Tu Trinh, Daniel S. Brown:
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning. CoRR abs/2211.15542 (2022) - [i21]Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown, Zackory Erickson, Anca D. Dragan:
Learning Representations that Enable Generalization in Assistive Tasks. CoRR abs/2212.03175 (2022) - 2021
- [c25]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: 502-509 - [c24]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: 1124-1131 - [c23]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: 598-608 - [c22]Daniel S. Brown, Jordan Schneider, Anca D. Dragan, Scott Niekum:
Value Alignment Verification. ICML 2021: 1105-1115 - [c21]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: 4785-4796 - [c20]Matthew Zurek, Andreea Bobu, Daniel S. Brown, Anca D. Dragan:
Situational Confidence Assistance for Lifelong Shared Autonomy. ICRA 2021: 2783-2789 - [c19]Arjun Sripathy, Andreea Bobu, Daniel S. Brown, Anca D. Dragan:
Dynamically Switching Human Prediction Models for Efficient Planning. ICRA 2021: 3495-3501 - [c18]Avik Jain, Lawrence Chan, Daniel S. Brown, Anca D. Dragan:
Optimal Cost Design for Model Predictive Control. L4DC 2021: 1205-1217 - [i20]Arjun Sripathy, Andreea Bobu, Daniel S. Brown, Anca D. Dragan:
Dynamically Switching Human Prediction Models for Efficient Planning. CoRR abs/2103.07815 (2021) - [i19]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 abs/2104.00053 (2021) - [i18]Matthew Zurek, Andreea Bobu, Daniel S. Brown, Anca D. Dragan:
Situational Confidence Assistance for Lifelong Shared Autonomy. CoRR abs/2104.06556 (2021) - [i17]Avik Jain, Lawrence Chan, Daniel S. Brown, Anca D. Dragan:
Optimal Cost Design for Model Predictive Control. CoRR abs/2104.11353 (2021) - [i16]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 abs/2106.06499 (2021) - [i15]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 abs/2107.05789 (2021) - [i14]Daniel Shin, Daniel S. Brown:
Offline Preference-Based Apprenticeship Learning. CoRR abs/2107.09251 (2021) - [i13]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 abs/2109.08273 (2021) - [i12]Letian Fu, Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Jeffrey Ichnowski, Eugen Solowjow, Ken Goldberg:
LEGS: Learning Efficient Grasp Sets for Exploratory Grasping. CoRR abs/2111.15002 (2021) - 2020
- [c17]Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Ken Goldberg:
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. CoRL 2020: 377-393 - [c16]Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum:
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. ICML 2020: 1165-1177 - [c15]Daniel S. Brown, Scott Niekum, Marek Petrik:
Bayesian Robust Optimization for Imitation Learning. NeurIPS 2020 - [i11]Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum:
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. CoRR abs/2002.09089 (2020) - [i10]Daniel S. Brown, Scott Niekum, Marek Petrik:
Bayesian Robust Optimization for Imitation Learning. CoRR abs/2007.12315 (2020) - [i9]Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown, Shivin Devgon, Ken Goldberg:
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects. CoRR abs/2011.05632 (2020) - [i8]Daniel S. Brown, Jordan Schneider, Scott Niekum:
Value Alignment Verification. CoRR abs/2012.01557 (2020)
2010 – 2019
- 2019
- [c14]Daniel S. Brown, Scott Niekum:
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. AAAI 2019: 7749-7758 - [c13]Daniel S. Brown, Wonjoon Goo, Scott Niekum:
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations. CoRL 2019: 330-359 - [c12]Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum:
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. ICML 2019: 783-792 - [i7]Daniel S. Brown, Yuchen Cui, Scott Niekum:
Risk-Aware Active Inverse Reinforcement Learning. CoRR abs/1901.02161 (2019) - [i6]Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum:
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. CoRR abs/1904.06387 (2019) - [i5]Daniel S. Brown, Wonjoon Goo, Scott Niekum:
Ranking-Based Reward Extrapolation without Rankings. CoRR abs/1907.03976 (2019) - [i4]Daniel S. Brown, Scott Niekum:
Deep Bayesian Reward Learning from Preferences. CoRR abs/1912.04472 (2019) - 2018
- [c11]Daniel S. Brown, Scott Niekum:
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. AAAI 2018: 2754-2762 - [c10]Daniel S. Brown, Yuchen Cui, Scott Niekum:
Risk-Aware Active Inverse Reinforcement Learning. CoRL 2018: 362-372 - [i3]Daniel S. Brown, Scott Niekum:
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. CoRR abs/1805.07687 (2018) - [i2]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 abs/1811.03563 (2018) - 2017
- [j2]Daniel S. Brown, Jeffrey Hudack, Nathaniel Gemelli, Bikramjit Banerjee:
Exact and Heuristic Algorithms for Risk-Aware Stochastic Physical Search. Comput. Intell. 33(3): 524-553 (2017) - [c9]Daniel S. Brown, Scott Niekum:
Toward Probabilistic Safety Bounds for Robot Learning from Demonstration. AAAI Fall Symposia 2017: 10-18 - [i1]Daniel S. Brown, Scott Niekum:
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. CoRR abs/1707.00724 (2017) - 2016
- [j1]Daniel S. Brown, Michael A. Goodrich, Shin-Young Jung, Sean Kerman:
Two invariants of human-swarm interaction. J. Hum. Robot Interact. 5(1): 1-31 (2016) - [c8]Daniel S. Brown, Ryan Turner, Oliver Hennigh, Steven Loscalzo:
Discovery and Exploration of Novel Swarm Behaviors Given Limited Robot Capabilities. DARS 2016: 447-460 - [c7]Matthew Berger, Lee M. Seversky, Daniel S. Brown:
Classifying swarm behavior via compressive subspace learning. ICRA 2016: 5328-5335 - 2015
- [c6]Daniel S. Brown, Steven Loscalzo, Nathaniel Gemelli:
k-Agent Sufficiency for Multiagent Stochastic Physical Search Problems. ADT 2015: 171-186 - [c5]Jeffrey Hudack, Nathaniel Gemelli, Daniel S. Brown, Steven Loscalzo, Jae C. Oh:
Multiobjective Optimization for the Stochastic Physical Search Problem. IEA/AIE 2015: 212-221 - 2014
- [c4]Daniel S. Brown, Michael A. Goodrich:
Limited bandwidth recognition of collective behaviors in bio-inspired swarms. AAMAS 2014: 405-412 - [c3]Daniel S. Brown, Sean C. Kerman, Michael A. Goodrich:
Human-swarm interactions based on managing attractors. HRI 2014: 90-97 - [c2]Daniel S. Brown, Shin-Young Jun, Michael A. Goodrich:
Balancing human and inter-agent influences for shared control of bio-inspired collectives. SMC 2014: 4123-4128 - 2013
- [c1]Shin-Young Jun, Daniel S. Brown, Michael A. Goodrich:
Shaping Couzin-Like Torus Swarms through Coordinated Mediation. SMC 2013: 1834-1839
Coauthor Index
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last updated on 2024-10-24 20:28 CEST by the dblp team
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