Evolved Intrinsic Reward Functions for Reinforcement Learning. - Scott Niekum (AAAI, 2010)

Genetic Programming for Reward Function Search. - Scott Niekum; Andrew G. Barto; Lee Spector (IEEE Trans. Auton. Ment. Dev., 2010)

Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery. - Scott Niekum; Andrew G. Barto (Lifelong Learning, 2011)

Evolution of reward functions for reinforcement learning. - Scott Niekum; Lee Spector; Andrew G. Barto (GECCO (Companion), 2011)

TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning. - George Dimitri Konidaris; Scott Niekum; Philip S. Thomas (NIPS, 2011)

Complex Task Learning from Unstructured Demonstrations. - Scott Niekum (AAAI, 2012)

Learning and generalization of complex tasks from unstructured demonstrations. - Scott Niekum; Sarah Osentoski; George Dimitri Konidaris; Andrew G. Barto (IROS, 2012)

An Integrated System for Learning Multi-Step Robotic Tasks from Unstructured Demonstrations. - Scott Niekum (AAAI Spring Symposium: Designing Intelligent Robots, 2013)

Incremental Semantically Grounded Learning from Demonstration. - Scott Niekum; Sachin Chitta; Andrew G. Barto; Bhaskara Marthi; Sarah Osentoski (Robotics: Science and Systems, 2013)

Learning pouring skills from demonstration and practice. - Akihiko Yamaguchi; Christopher G. Atkeson; Scott Niekum; Tsukasa Ogasawara (Humanoids, 2014)

Active articulation model estimation through interactive perception. - Karol Hausman; Scott Niekum; Sarah Osentoski; Gaurav S. Sukhatme (ICRA, 2015)

Online Bayesian changepoint detection for articulated motion models. - Scott Niekum; Sarah Osentoski; Christopher G. Atkeson; Andrew G. Barto (ICRA, 2015)

Policy Evaluation Using the Ω-Return. - Philip S. Thomas; Scott Niekum; Georgios Theocharous; George Dimitri Konidaris (NIPS, 2015)

Learning grounded finite-state representations from unstructured demonstrations. - Scott Niekum; Sarah Osentoski; George Dimitri Konidaris; Sachin Chitta; Bhaskara Marthi; Andrew G. Barto (Int. J. Robotics Res., 2015)

On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search. - Piyush Khandelwal; Elad Liebman; Scott Niekum; Peter Stone 001 (ICML, 2016)

High Confidence Off-Policy Evaluation with Models. - Josiah P. Hanna; Peter Stone 001; Scott Niekum (CoRR, 2016)

Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation. - Josiah P. Hanna; Peter Stone 001; Scott Niekum (AAAI, 2017)

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

Data-Efficient Policy Evaluation Through Behavior Policy Search. - Josiah P. Hanna; Philip S. Thomas; Peter Stone 001; Scott Niekum (ICML, 2017)

Classification error correction: A case study in brain-computer interfacing. - Hasan A. Poonawala; Mohammed Alshiekh; Scott Niekum; Ufuk Topcu (IROS, 2017)

Viewpoint selection for visual failure detection. - Akanksha Saran; Branka Lakic; Srinjoy Majumdar; Jürgen Hess 003; Scott Niekum (IROS, 2017)

Safe Reinforcement Learning via Shielding. - Mohammed Alshiekh; Roderick Bloem; Rüdiger Ehlers; Bettina Könighofer; Scott Niekum; Ufuk Topcu (AAAI, 2018)

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)

Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics. - Ajinkya Jain; Scott Niekum (CoRL, 2018)

Asking for Help Effectively via Modeling of Human Beliefs. - Taylor Kessler Faulkner; Scott Niekum; Andrea Thomaz (HRI (Companion), 2018)

Active Reward Learning from Critiques. - Yuchen Cui; Scott Niekum (ICRA, 2018)

Incremental Task Modification via Corrective Demonstrations. - Reymundo A. Gutierrez; Vivian Chu; Andrea Lockerd Thomaz; Scott Niekum (ICRA, 2018)

Human Gaze Following for Human-Robot Interaction. - Akanksha Saran; Srinjoy Majumdar; Elaine Schaertl Short; Andrea Thomaz; Scott Niekum (IROS, 2018)

Learning Multi-Step Robotic Tasks from Observation. - Wonjoon Goo; Scott Niekum (CoRR, 2018)

Towards Online Learning from Corrective Demonstrations. - Reymundo A. Gutierrez; Elaine Schaertl Short; Scott Niekum; Andrea Lockerd Thomaz (CoRR, 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)

Understanding Teacher Gaze Patterns for Robot Learning. - Akanksha Saran; Elaine Schaertl Short; Andrea Thomaz; Scott Niekum (CoRL, 2019)

Learning from Corrective Demonstrations. - Reymundo A. Gutierrez; Elaine Schaertl Short; Scott Niekum; Andrea Lockerd Thomaz (HRI, 2019)

Enhancing Robot Learning with Human Social Cues. - Akanksha Saran; Elaine Schaertl Short; Andrea Thomaz; Scott Niekum (HRI, 2019)

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

Importance Sampling Policy Evaluation with an Estimated Behavior Policy. - Josiah Hanna; Scott Niekum; Peter Stone 001 (ICML, 2019)

Uncertainty-Aware Data Aggregation for Deep Imitation Learning. - Yuchen Cui; David Isele; Scott Niekum; Kikuo Fujimura (ICRA, 2019)

One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video. - Wonjoon Goo; Scott Niekum (ICRA, 2019)

Using Natural Language for Reward Shaping in Reinforcement Learning. - Prasoon Goyal; Scott Niekum; Raymond J. Mooney (IJCAI, 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)

The EMPATHIC Framework for Task Learning from Implicit Human Feedback. - Yuchen Cui; Qiping Zhang; W. Bradley Knox; Alessandro Allievi; Peter Stone 001; Scott Niekum (CoRL, 2020)

PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards. - Prasoon Goyal; Scott Niekum; Raymond J. Mooney (CoRL, 2020)

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

Human Gaze Assisted Artificial Intelligence: A Review. - Ruohan Zhang; Akanksha Saran; Bo Liu 042; Yifeng Zhu; Sihang Guo; Scott Niekum; Dana H. Ballard; Mary M. Hayhoe (IJCAI, 2020)

Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning. - Caleb Chuck; Supawit Chockchowwat; Scott Niekum (IROS, 2020)

Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty. - Ajinkya Jain; Scott Niekum (IROS, 2020)

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

Local Nonparametric Meta-Learning. - Wonjoon Goo; Scott Niekum (CoRR, 2020)

Efficiently Guiding Imitation Learning Algorithms with Human Gaze. - Akanksha Saran; Ruohan Zhang; Elaine Schaertl Short; Scott Niekum (CoRR, 2020)

ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory. - Ajinkya Jain; Rudolf Lioutikov; Scott Niekum (CoRR, 2020)

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

Demonstration of the EMPATHIC Framework for Task Learning from Implicit Human Feedback. - Yuchen Cui; Qiping Zhang; Sahil Jain; Alessandro Allievi; Peter Stone 001; Scott Niekum; W. Bradley Knox (AAAI, 2021)

Efficiently Guiding Imitation Learning Agents with Human Gaze. - Akanksha Saran; Ruohan Zhang; Elaine Schaertl Short; Scott Niekum (AAMAS, 2021)

You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL. - Wonjoon Goo; Scott Niekum (CoRL, 2021)

Distributional Depth-Based Estimation of Object Articulation Models. - Ajinkya Jain; Stephen Giguere 001; Rudolf Lioutikov; Scott Niekum (CoRL, 2021)

SCAPE: Learning Stiffness Control from Augmented Position Control Experiences. - Mincheol Kim; Scott Niekum; Ashish D. Deshpande (CoRL, 2021)

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

ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory. - Ajinkya Jain; Rudolf Lioutikov; Caleb Chuck; Scott Niekum (ICRA, 2021)

Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning. - Yuchen Cui; Pallavi Koppol; Henny Admoni; Scott Niekum; Reid G. Simmons; Aaron Steinfeld; Tesca Fitzgerald (IJCAI, 2021)

Self-Supervised Online Reward Shaping in Sparse-Reward Environments. - Farzan Memarian; Wonjoon Goo; Rudolf Lioutikov; Scott Niekum; Ufuk Topcu (IROS, 2021)

Universal Off-Policy Evaluation. - Yash Chandak; Scott Niekum; Bruno C. da Silva 001; Erik G. Learned-Miller; Emma Brunskill; Philip S. Thomas (NeurIPS, 2021)

Adversarial Intrinsic Motivation for Reinforcement Learning. - Ishan Durugkar; Mauricio Tec; Scott Niekum; Peter Stone 001 (NeurIPS, 2021)

SOPE: Spectrum of Off-Policy Estimators. - Christina J. Yuan; Yash Chandak; Stephen Giguere 001; Philip S. Thomas; Scott Niekum (NeurIPS, 2021)

Zero-shot Task Adaptation using Natural Language. - Prasoon Goyal; Raymond J. Mooney; Scott Niekum (CoRR, 2021)

Robust Generative Adversarial Imitation Learning via Local Lipschitzness. - Farzan Memarian; Abolfazl Hashemi; Scott Niekum; Ufuk Topcu (CoRR, 2021)

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms. - Oliver Kroemer; Scott Niekum; George Konidaris 001 (J. Mach. Learn. Res., 2021)

Importance sampling in reinforcement learning with an estimated behavior policy. - Josiah P. Hanna; Scott Niekum; Peter Stone 001 (Mach. Learn., 2021)

Fairness Guarantees under Demographic Shift. - Stephen Giguere 001; Blossom Metevier; Bruno Castro da Silva; Yuriy Brun; Philip S. Thomas; Scott Niekum (ICLR, 2022)

Understanding Acoustic Patterns of Human Teachers Demonstrating Manipulation Tasks to Robots. - Akanksha Saran; Kush Desai; Mai Lee Chang; Rudolf Lioutikov; Andrea Thomaz; Scott Niekum (IROS, 2022)

Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? - Yuchen Cui; Scott Niekum; Abhinav Gupta 001; Vikash Kumar; Aravind Rajeswaran (L4DC, 2022)

Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL. - Wonjoon Goo; Scott Niekum (CoRR, 2022)

The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications. - Serena Booth; W. Bradley Knox; Julie Shah; Scott Niekum; Peter Stone 001; Alessandro Allievi (AAAI, 2023)

Language-guided Task Adaptation for Imitation Learning. - Prasoon Goyal; Raymond J. Mooney; Scott Niekum (CoRR, 2023)

Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods. - Harshit Sikchi; Amy Zhang 001; Scott Niekum (CoRR, 2023)

Granger-Causal Hierarchical Skill Discovery. - Caleb Chuck; Kevin Black; Aditya Arjun; Yuke Zhu; Scott Niekum (CoRR, 2023)

Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill-Learning. - Andrew Levy; Sreehari Rammohan; Alessandro Allievi; Scott Niekum; George Konidaris 001 (CoRR, 2023)

Contrastive Preference Learning: Learning from Human Feedback without RL. - Joey Hejna; Rafael Rafailov; Harshit Sikchi; Chelsea Finn; Scott Niekum; W. Bradley Knox; Dorsa Sadigh (CoRR, 2023)

A Ranking Game for Imitation Learning. - Harshit Sikchi; Akanksha Saran; Wonjoon Goo; Scott Niekum (Trans. Mach. Learn. Res., 2023)

Learning Optimal Advantage from Preferences and Mistaking It for Reward. - W. Bradley Knox; Stephane Hatgis-Kessell; Sigurdur O. Adalgeirsson; Serena Booth; Anca D. Dragan; Peter Stone 001; Scott Niekum (AAAI, 2024)

A Dual Approach to Imitation Learning from Observations with Offline Datasets. - Harshit Sikchi; Caleb Chuck; Amy Zhang 001; Scott Niekum (CoRL, 2024)

Contrastive Preference Learning: Learning from Human Feedback without Reinforcement Learning. - Joey Hejna; Rafael Rafailov; Harshit Sikchi; Chelsea Finn; Scott Niekum; W. Bradley Knox; Dorsa Sadigh (ICLR, 2024)

Score Models for Offline Goal-Conditioned Reinforcement Learning. - Harshit Sikchi; Rohan Chitnis; Ahmed Touati; Alborz Geramifard; Amy Zhang 001; Scott Niekum (ICLR, 2024)

Dual RL: Unification and New Methods for Reinforcement and Imitation Learning. - Harshit Sikchi; Qinqing Zheng; Amy Zhang 001; Scott Niekum (ICLR, 2024)

Gaze Supervision for Mitigating Causal Confusion in Driving Agents. - Abhijat Biswas; Badal Arun Pardhi; Caleb Chuck; Jarrett Holtz; Scott Niekum; Henny Admoni; Alessandro Allievi (IV, 2024)

Predicting Future Actions of Reinforcement Learning Agents. - Stephen Chung; Scott Niekum; David Krueger 001 (NeurIPS, 2024)

Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms. - Rafael Rafailov; Yaswanth Chittepu; Ryan Park; Harshit Sikchi; Joey Hejna; W. Bradley Knox; Chelsea Finn; Scott Niekum (NeurIPS, 2024)

SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions. - Zizhao Wang; Jiaheng Hu; Caleb Chuck; Stephen Chen; Roberto Martín-Martín; Amy Zhang 001; Scott Niekum; Peter Stone 001 (NeurIPS, 2024)

Learning Action-based Representations Using Invariance. - Max Rudolph; Caleb Chuck; Kevin Black; Misha Lvovsky; Scott Niekum; Amy Zhang 001 (RLJ, 2024)

Automated Discovery of Functional Actual Causes in Complex Environments. - Caleb Chuck; Sankaran Vaidyanathan; Stephen Giguere 001; Amy Zhang 001; David Jensen; Scott Niekum (CoRR, 2024)

D2PO: Discriminator-Guided DPO with Response Evaluation Models. - Prasann Singhal; Nathan Lambert 001; Scott Niekum; Tanya Goyal; Greg Durrett (CoRR, 2024)

Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning. - 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; et al. (CoRR, 2024)

Pareto-Optimal Learning from Preferences with Hidden Context. - Ryan Boldi; Li Ding 010; Lee Spector; Scott Niekum (CoRR, 2024)

RL Zero: Zero-Shot Language to Behaviors without any Supervision. - Harshit Sikchi; Siddhant Agarwal; Pranaya Jajoo; Samyak Parajuli; Caleb Chuck; Max Rudolph; Peter Stone 001; Amy Zhang 001; Scott Niekum (CoRR, 2024)

Granger Causal Interaction Skill Chains. - Caleb Chuck; Kevin Black; Aditya Arjun; Yuke Zhu; Scott Niekum (Trans. Mach. Learn. Res., 2024)

Models of human preference for learning reward functions. - W. Bradley Knox; Stephane Hatgis-Kessell; Serena Booth; Scott Niekum; Peter Stone 001; Alessandro Gabriele Allievi (Trans. Mach. Learn. Res., 2024)

Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning. - Caleb Chuck; Fan Feng; Carl Qi; Chang Shi; Siddhant Agarwal; Amy Zhang 001; Scott Niekum (ICLR, 2025)

An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning. - Haoran Xu 003; Shuozhe Li; Harshit Sikchi; Scott Niekum; Amy Zhang 001 (ICLR, 2025)

Influencing Humans to Conform to Preference Models for RLHF. - Stephane Hatgis-Kessell; W. Bradley Knox; Serena Booth; Scott Niekum; Peter Stone 001 (CoRR, 2025)

Supervised Reward Inference. - Will Schwarzer; Jordan Schneider; Philip S. Thomas; Scott Niekum (CoRR, 2025)

Fast Adaptation with Behavioral Foundation Models. - Harshit Sikchi; Andrea Tirinzoni; Ahmed Touati; Yingchen Xu; Anssi Kanervisto; Scott Niekum; Amy Zhang 001; Alessandro Lazaric; Matteo Pirotta (CoRR, 2025)

Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluation. - Tuhina Tripathi; Manya Wadhwa; Greg Durrett; Scott Niekum (CoRR, 2025)

A Descriptive and Normative Theory of Human Beliefs in RLHF. - Sylee Dandekar; Shripad Deshmukh; Frank Chiu; W. Bradley Knox; Scott Niekum (CoRR, 2025)

Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints. - Yaswanth Chittepu; Blossom Metevier; Will Schwarzer; Austin Hoag; Scott Niekum; Philip S. Thomas (CoRR, 2025)