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2020 – today
- 2024
[j7]Caleb Chuck, Kevin Black, Aditya Arjun, Yuke Zhu, Scott Niekum:
Granger Causal Interaction Skill Chains. Trans. Mach. Learn. Res. 2024 (2024)- [j6]W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro Gabriele Allievi:
Models of human preference for learning reward functions. Trans. Mach. Learn. Res. 2024 (2024) - [c67]W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur O. Adalgeirsson, Serena Booth, Anca D. Dragan, Peter Stone, Scott Niekum:
Learning Optimal Advantage from Preferences and Mistaking It for Reward. AAAI 2024: 10066-10073 - [c66]Abhijat Biswas, Badal Arun Pardhi, Caleb Chuck, Jarrett Holtz, Scott Niekum, Henny Admoni, Alessandro Allievi:
Gaze Supervision for Mitigating Causal Confusion in Driving Agents. AAMAS 2024: 2159-2161 - [c65]Joey Hejna, Rafael Rafailov, Harshit Sikchi, Chelsea Finn, Scott Niekum, W. Bradley Knox, Dorsa Sadigh:
Contrastive Preference Learning: Learning from Human Feedback without Reinforcement Learning. ICLR 2024 - [c64]Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum:
Score Models for Offline Goal-Conditioned Reinforcement Learning. ICLR 2024 - [c63]Harshit Sikchi, Qinqing Zheng, Amy Zhang, Scott Niekum:
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning. ICLR 2024 - [c62]Abhijat Biswas, Badal Arun Pardhi, Caleb Chuck, Jarrett Holtz, Scott Niekum, Henny Admoni, Alessandro Allievi:
Gaze Supervision for Mitigating Causal Confusion in Driving Agents. IV 2024: 2331-2338 - [i56]Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum, Amy Zhang:
Learning Action-based Representations Using Invariance. CoRR abs/2403.16369 (2024) - [i55]Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere, Amy Zhang, David Jensen, Scott Niekum:
Automated Discovery of Functional Actual Causes in Complex Environments. CoRR abs/2404.10883 (2024) - [i54]Prasann Singhal, Nathan Lambert, Scott Niekum, Tanya Goyal, Greg Durrett:
D2PO: Discriminator-Guided DPO with Response Evaluation Models. CoRR abs/2405.01511 (2024) - [i53]Caleb Chuck, Carl Qi, Michael J. Munje, Shuozhe Li, Max Rudolph, Chang Shi, Siddhant Agarwal, Harshit Sikchi, Abhinav Peri, Sarthak Dayal, Evan Kuo, Kavan Mehta, Anthony Wang, Peter Stone, Amy Zhang, Scott Niekum:
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning. CoRR abs/2405.03113 (2024) - [i52]Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, W. Bradley Knox, Chelsea Finn, Scott Niekum:
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms. CoRR abs/2406.02900 (2024) - [i51]Harshit Sikchi, Caleb Chuck, Amy Zhang, Scott Niekum:
A Dual Approach to Imitation Learning from Observations with Offline Datasets. CoRR abs/2406.08805 (2024) - [i50]Ryan Boldi, Li Ding, Lee Spector, Scott Niekum:
Pareto-Optimal Learning from Preferences with Hidden Context. CoRR abs/2406.15599 (2024) - 2023
- [j5]Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum:
A Ranking Game for Imitation Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c61]Serena Booth, W. Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, Alessandro Allievi:
The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications. AAAI 2023: 5920-5929 - [i49]Prasoon Goyal, Raymond J. Mooney, Scott Niekum:
Language-guided Task Adaptation for Imitation Learning. CoRR abs/2301.09770 (2023) - [i48]Harshit Sikchi, Amy Zhang, Scott Niekum:
Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods. CoRR abs/2302.08560 (2023) - [i47]Caleb Chuck, Kevin Black, Aditya Arjun, Yuke Zhu, Scott Niekum:
Granger-Causal Hierarchical Skill Discovery. CoRR abs/2306.09509 (2023) - [i46]Andrew Levy, Sreehari Rammohan, Alessandro Allievi, Scott Niekum, George Konidaris:
Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill-Learning. CoRR abs/2307.02728 (2023) - [i45]W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur O. Adalgeirsson, Serena Booth, Anca D. Dragan, Peter Stone, Scott Niekum:
Learning Optimal Advantage from Preferences and Mistaking it for Reward. CoRR abs/2310.02456 (2023) - [i44]Joey Hejna, Rafael Rafailov, Harshit Sikchi, Chelsea Finn, Scott Niekum, W. Bradley Knox, Dorsa Sadigh:
Contrastive Preference Learning: Learning from Human Feedback without RL. CoRR abs/2310.13639 (2023) - [i43]Harshit Sikchi, Rohan Chitnis, Ahmed Touati, Alborz Geramifard, Amy Zhang, Scott Niekum:
Score Models for Offline Goal-Conditioned Reinforcement Learning. CoRR abs/2311.02013 (2023) - 2022
- [c60]Stephen Giguere, Blossom Metevier, Bruno Castro da Silva, Yuriy Brun, Philip S. Thomas, Scott Niekum:
Fairness Guarantees under Demographic Shift. ICLR 2022 - [c59]Akanksha Saran, Kush Desai, Mai Lee Chang, Rudolf Lioutikov, Andrea Thomaz, Scott Niekum:
Understanding Acoustic Patterns of Human Teachers Demonstrating Manipulation Tasks to Robots. IROS 2022: 9172-9179 - [c58]Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran:
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? L4DC 2022: 893-905 - [i42]Harshit Sikchi, Akanksha Saran, Wonjoon Goo, Scott Niekum:
A Ranking Game for Imitation Learning. CoRR abs/2202.03481 (2022) - [i41]Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, Aravind Rajeswaran:
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? CoRR abs/2204.11134 (2022) - [i40]Wonjoon Goo, Scott Niekum:
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL. CoRR abs/2206.00695 (2022) - [i39]W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro Allievi:
Models of human preference for learning reward functions. CoRR abs/2206.02231 (2022) - [i38]Akanksha Saran, Kush Desai, Mai Lee Chang, Rudolf Lioutikov, Andrea Thomaz, Scott Niekum:
Understanding Acoustic Patterns of Human Teachers Demonstrating Manipulation Tasks to Robots. CoRR abs/2211.00352 (2022) - 2021
- [j4]Oliver Kroemer, Scott Niekum, George Konidaris:
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms. J. Mach. Learn. Res. 22: 30:1-30:82 (2021) - [j3]Josiah P. Hanna
, Scott Niekum, Peter Stone:
Importance sampling in reinforcement learning with an estimated behavior policy. Mach. Learn. 110(6): 1267-1317 (2021) - [c57]Yuchen Cui, Qiping Zhang, Sahil Jain, Alessandro Allievi, Peter Stone, Scott Niekum, W. Bradley Knox:
Demonstration of the EMPATHIC Framework for Task Learning from Implicit Human Feedback. AAAI 2021: 16017-16019 - [c56]Akanksha Saran, Ruohan Zhang, Elaine Schaertl Short, Scott Niekum:
Efficiently Guiding Imitation Learning Agents with Human Gaze. AAMAS 2021: 1109-1117 - [c55]Mincheol Kim, Scott Niekum, Ashish D. Deshpande:
SCAPE: Learning Stiffness Control from Augmented Position Control Experiences. CoRL 2021: 1512-1521 - [c54]Wonjoon Goo, Scott Niekum:
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL. CoRL 2021: 1543-1553 - [c53]Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov, Scott Niekum:
Distributional Depth-Based Estimation of Object Articulation Models. CoRL 2021: 1611-1621 - [c52]Daniel S. Brown, Jordan Schneider, Anca D. Dragan, Scott Niekum:
Value Alignment Verification. ICML 2021: 1105-1115 - [c51]Ajinkya Jain, Rudolf Lioutikov, Caleb Chuck, Scott Niekum:
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory. ICRA 2021: 13670-13677 - [c50]Yuchen Cui, Pallavi Koppol, Henny Admoni, Scott Niekum, Reid G. Simmons, Aaron Steinfeld, Tesca Fitzgerald:
Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning. IJCAI 2021: 4382-4391 - [c49]Farzan Memarian, Wonjoon Goo, Rudolf Lioutikov, Scott Niekum, Ufuk Topcu:
Self-Supervised Online Reward Shaping in Sparse-Reward Environments. IROS 2021: 2369-2375 - [c48]Ishan Durugkar, Mauricio Tec, Scott Niekum, Peter Stone:
Adversarial Intrinsic Motivation for Reinforcement Learning. NeurIPS 2021: 8622-8636 - [c47]Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum:
SOPE: Spectrum of Off-Policy Estimators. NeurIPS 2021: 18958-18969 - [c46]Yash Chandak, Scott Niekum, Bruno C. da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas:
Universal Off-Policy Evaluation. NeurIPS 2021: 27475-27490 - [i37]Mincheol Kim, Scott Niekum, Ashish D. Deshpande:
SCAPE: Learning Stiffness Control from Augmented Position Control Experiences. CoRR abs/2102.08442 (2021) - [i36]Farzan Memarian, Wonjoon Goo, Rudolf Lioutikov, Ufuk Topcu, Scott Niekum:
Self-Supervised Online Reward Shaping in Sparse-Reward Environments. CoRR abs/2103.04529 (2021) - [i35]Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas:
Universal Off-Policy Evaluation. CoRR abs/2104.12820 (2021) - [i34]Ishan Durugkar, Mauricio Tec, Scott Niekum, Peter Stone:
Adversarial Intrinsic Motivation for Reinforcement Learning. CoRR abs/2105.13345 (2021) - [i33]Prasoon Goyal, Raymond J. Mooney, Scott Niekum:
Zero-shot Task Adaptation using Natural Language. CoRR abs/2106.02972 (2021) - [i32]Farzan Memarian, Abolfazl Hashemi, Scott Niekum, Ufuk Topcu:
Robust Generative Adversarial Imitation Learning via Local Lipschitzness. CoRR abs/2107.00116 (2021) - [i31]Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov, Scott Niekum:
Distributional Depth-Based Estimation of Object Articulation Models. CoRR abs/2108.05875 (2021) - [i30]Wonjoon Goo, Scott Niekum:
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL. CoRR abs/2110.02304 (2021) - [i29]Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum:
SOPE: Spectrum of Off-Policy Estimators. CoRR abs/2111.03936 (2021) - 2020
- [c45]Prasoon Goyal, Scott Niekum, Raymond J. Mooney:
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards. CoRL 2020: 485-497 - [c44]Yuchen Cui, Qiping Zhang, W. Bradley Knox, Alessandro Allievi, Peter Stone, Scott Niekum:
The EMPATHIC Framework for Task Learning from Implicit Human Feedback. CoRL 2020: 604-626 - [c43]Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum:
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. ICML 2020: 1165-1177 - [c42]Ruohan Zhang, Akanksha Saran, Bo Liu, Yifeng Zhu, Sihang Guo, Scott Niekum, Dana H. Ballard, Mary M. Hayhoe:
Human Gaze Assisted Artificial Intelligence: A Review. IJCAI 2020: 4951-4958 - [c41]Ajinkya Jain, Scott Niekum:
Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty. IROS 2020: 5253-5260 - [c40]Caleb Chuck, Supawit Chockchowwat, Scott Niekum:
Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning. IROS 2020: 5572-5579 - [c39]Daniel S. Brown, Scott Niekum, Marek Petrik:
Bayesian Robust Optimization for Imitation Learning. NeurIPS 2020 - [i28]Wonjoon Goo, Scott Niekum:
Local Nonparametric Meta-Learning. CoRR abs/2002.03272 (2020) - [i27]Daniel S. Brown, Russell Coleman, Ravi Srinivasan, Scott Niekum:
Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences. CoRR abs/2002.09089 (2020) - [i26]Akanksha Saran, Ruohan Zhang, Elaine Schaertl Short, Scott Niekum:
Efficiently Guiding Imitation Learning Algorithms with Human Gaze. CoRR abs/2002.12500 (2020) - [i25]Daniel S. Brown, Scott Niekum, Marek Petrik:
Bayesian Robust Optimization for Imitation Learning. CoRR abs/2007.12315 (2020) - [i24]Prasoon Goyal, Scott Niekum, Raymond J. Mooney:
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards. CoRR abs/2007.15543 (2020) - [i23]Ajinkya Jain, Rudolf Lioutikov, Scott Niekum:
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory. CoRR abs/2008.10518 (2020) - [i22]Yuchen Cui, Qiping Zhang, Alessandro Allievi, Peter Stone, Scott Niekum, W. Bradley Knox:
The EMPATHIC Framework for Task Learning from Implicit Human Feedback. CoRR abs/2009.13649 (2020) - [i21]Daniel S. Brown, Jordan Schneider, Scott Niekum:
Value Alignment Verification. CoRR abs/2012.01557 (2020)
2010 – 2019
- 2019
- [c38]Daniel S. Brown, Scott Niekum:
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. AAAI 2019: 7749-7758 - [c37]Daniel S. Brown, Wonjoon Goo, Scott Niekum:
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations. CoRL 2019: 330-359 - [c36]Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz, Scott Niekum:
Understanding Teacher Gaze Patterns for Robot Learning. CoRL 2019: 1247-1258 - [c35]Reymundo A. Gutierrez, Elaine Schaertl Short, Scott Niekum, Andrea Lockerd Thomaz:
Learning from Corrective Demonstrations. HRI 2019: 712-714 - [c34]Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz, Scott Niekum:
Enhancing Robot Learning with Human Social Cues. HRI 2019: 745-747 - [c33]Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum:
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. ICML 2019: 783-792 - [c32]Josiah Hanna, Scott Niekum, Peter Stone:
Importance Sampling Policy Evaluation with an Estimated Behavior Policy. ICML 2019: 2605-2613 - [c31]Yuchen Cui, David Isele, Scott Niekum, Kikuo Fujimura:
Uncertainty-Aware Data Aggregation for Deep Imitation Learning. ICRA 2019: 761-767 - [c30]Wonjoon Goo, Scott Niekum:
One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video. ICRA 2019: 7755-7761 - [c29]Prasoon Goyal, Scott Niekum, Raymond J. Mooney:
Using Natural Language for Reward Shaping in Reinforcement Learning. IJCAI 2019: 2385-2391 - [i20]Daniel S. Brown, Yuchen Cui, Scott Niekum:
Risk-Aware Active Inverse Reinforcement Learning. CoRR abs/1901.02161 (2019) - [i19]Prasoon Goyal, Scott Niekum, Raymond J. Mooney:
Using Natural Language for Reward Shaping in Reinforcement Learning. CoRR abs/1903.02020 (2019) - [i18]Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum:
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. CoRR abs/1904.06387 (2019) - [i17]Yuchen Cui, David Isele, Scott Niekum, Kikuo Fujimura:
Uncertainty-Aware Data Aggregation for Deep Imitation Learning. CoRR abs/1905.02780 (2019) - [i16]Caleb Chuck, Supawit Chockchowwat, Scott Niekum:
Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning. CoRR abs/1906.01408 (2019) - [i15]Oliver Kroemer, Scott Niekum, George Dimitri Konidaris:
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms. CoRR abs/1907.03146 (2019) - [i14]Daniel S. Brown, Wonjoon Goo, Scott Niekum:
Ranking-Based Reward Extrapolation without Rankings. CoRR abs/1907.03976 (2019) - [i13]Akanksha Saran, Elaine Schaertl Short, Andrea Thomaz, Scott Niekum:
Understanding Teacher Gaze Patterns for Robot Learning. CoRR abs/1907.07202 (2019) - [i12]Ajinkya Jain, Scott Niekum:
Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty. CoRR abs/1907.09014 (2019) - [i11]Daniel S. Brown, Scott Niekum:
Deep Bayesian Reward Learning from Preferences. CoRR abs/1912.04472 (2019) - 2018
- [c28]Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, Ufuk Topcu:
Safe Reinforcement Learning via Shielding. AAAI 2018: 2669-2678 - [c27]Daniel S. Brown, Scott Niekum:
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. AAAI 2018: 2754-2762 - [c26]Daniel S. Brown, Yuchen Cui, Scott Niekum:
Risk-Aware Active Inverse Reinforcement Learning. CoRL 2018: 362-372 - [c25]Ajinkya Jain, Scott Niekum:
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics. CoRL 2018: 757-766 - [c24]Taylor Kessler Faulkner, Scott Niekum, Andrea Thomaz:
Asking for Help Effectively via Modeling of Human Beliefs. HRI (Companion) 2018: 149-150 - [c23]Reymundo A. Gutierrez, Vivian Chu, Andrea Lockerd Thomaz, Scott Niekum:
Incremental Task Modification via Corrective Demonstrations. ICRA 2018: 1126-1133 - [c22]Yuchen Cui, Scott Niekum:
Active Reward Learning from Critiques. ICRA 2018: 6907-6914 - [c21]Akanksha Saran, Srinjoy Majumdar, Elaine Schaertl Short, Andrea Thomaz, Scott Niekum:
Human Gaze Following for Human-Robot Interaction. IROS 2018: 8615-8621 - [i10]Ajinkya Jain, Scott Niekum:
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics. CoRR abs/1802.04205 (2018) - [i9]Daniel S. Brown, Scott Niekum:
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications. CoRR abs/1805.07687 (2018) - [i8]Josiah Hanna, Scott Niekum, Peter Stone:
Importance Sampling Policy Evaluation with an Estimated Behavior Policy. CoRR abs/1806.01347 (2018) - [i7]Wonjoon Goo, Scott Niekum:
Learning Multi-Step Robotic Tasks from Observation. CoRR abs/1806.11244 (2018) - [i6]Reymundo A. Gutierrez, Elaine Schaertl Short, Scott Niekum, Andrea Lockerd Thomaz:
Towards Online Learning from Corrective Demonstrations. CoRR abs/1810.01036 (2018) - [i5]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
- [c20]Josiah P. Hanna, Peter Stone, Scott Niekum:
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation. AAAI 2017: 4933-4934 - [c19]Daniel S. Brown, Scott Niekum:
Toward Probabilistic Safety Bounds for Robot Learning from Demonstration. AAAI Fall Symposia 2017: 10-18 - [c18]Josiah P. Hanna, Peter Stone, Scott Niekum:
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation. AAMAS 2017: 538-546 - [c17]Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum:
Data-Efficient Policy Evaluation Through Behavior Policy Search. ICML 2017: 1394-1403 - [c16]Hasan A. Poonawala, Mohammed Alshiekh, Scott Niekum, Ufuk Topcu:
Classification error correction: A case study in brain-computer interfacing. IROS 2017: 3006-3012 - [c15]Akanksha Saran, Branka Lakic, Srinjoy Majumdar, Juergen Hess, Scott Niekum:
Viewpoint selection for visual failure detection. IROS 2017: 5437-5444 - [i4]Josiah P. Hanna, Philip S. Thomas, Peter Stone, Scott Niekum:
Data-Efficient Policy Evaluation Through Behavior Policy Search. CoRR abs/1706.03469 (2017) - [i3]Daniel S. Brown, Scott Niekum:
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning. CoRR abs/1707.00724 (2017) - [i2]Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, Ufuk Topcu:
Safe Reinforcement Learning via Shielding. CoRR abs/1708.08611 (2017) - 2016
- [c14]Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone:
On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search. ICML 2016: 1319-1328 - [i1]Josiah P. Hanna, Peter Stone, Scott Niekum:
High Confidence Off-Policy Evaluation with Models. CoRR abs/1606.06126 (2016) - 2015
- [j2]Scott Niekum, Sarah Osentoski, George Dimitri Konidaris, Sachin Chitta, Bhaskara Marthi, Andrew G. Barto:
Learning grounded finite-state representations from unstructured demonstrations. Int. J. Robotics Res. 34(2): 131-157 (2015) - [c13]Scott Niekum, Sarah Osentoski, Christopher G. Atkeson, Andrew G. Barto:
Online Bayesian changepoint detection for articulated motion models. ICRA 2015: 1468-1475 - [c12]Karol Hausman, Scott Niekum, Sarah Osentoski, Gaurav S. Sukhatme:
Active articulation model estimation through interactive perception. ICRA 2015: 3305-3312 - [c11]Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Dimitri Konidaris:
Policy Evaluation Using the Ω-Return. NIPS 2015: 334-342 - 2014
- [c10]Akihiko Yamaguchi, Christopher G. Atkeson, Scott Niekum, Tsukasa Ogasawara:
Learning pouring skills from demonstration and practice. Humanoids 2014: 908-915 - 2013
- [c9]Scott Niekum:
An Integrated System for Learning Multi-Step Robotic Tasks from Unstructured Demonstrations. AAAI Spring Symposium: Designing Intelligent Robots 2013 - [c8]Scott Niekum, Sachin Chitta, Andrew G. Barto, Bhaskara Marthi, Sarah Osentoski:
Incremental Semantically Grounded Learning from Demonstration. Robotics: Science and Systems 2013 - 2012
- [c7]Scott Niekum:
Complex Task Learning from Unstructured Demonstrations. AAAI 2012: 2402-2403 - [c6]Scott Niekum, Sarah Osentoski, George Dimitri Konidaris, Andrew G. Barto:
Learning and generalization of complex tasks from unstructured demonstrations. IROS 2012: 5239-5246 - 2011
- [c5]Scott Niekum, Andrew G. Barto:
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery. Lifelong Learning 2011 - [c4]Scott Niekum, Lee Spector, Andrew G. Barto:
Evolution of reward functions for reinforcement learning. GECCO (Companion) 2011: 177-178 - [c3]