Design of a new hybrid control and knee orthosis for human walking and rehabilitation. - Tzu-Hao Huang; Han-Pang Huang; Ching-An Cheng; Jiun-Yih Kuan; Po-Ting Lee; Shih-Yi Huang (IROS, 2012)

Bayesian human intention estimator for exoskeleton system. - Ching-An Cheng; Tzu-Hao Huang; Han-Pang Huang (AIM, 2013)

Self-learning assistive exoskeleton with sliding mode admittance control. - Tzu-Hao Huang; Ching-An Cheng; Han-Pang Huang (IROS, 2013)

Humanoid robot push-recovery strategy based on CMP criterion and angular momentum regulation. - Che-Hsuan Chang; Han-Pang Huang; Huan-Kun Hsu; Ching-An Cheng (AIM, 2015)

Efficient grasp synthesis and control strategy for robot hand-arm system. - Ming-Bao Huang; Han-Pang Huang; Chih-Chun Cheng; Ching-An Cheng (CASE, 2015)

Incremental Variational Sparse Gaussian Process Regression. - Ching-An Cheng; Byron Boots (NIPS, 2016)

Virtual Impedance Control for Safe Human-Robot Interaction. - Sheng-Yen Lo; Ching-An Cheng; Han-Pang Huang (J. Intell. Robotic Syst., 2016)

Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems. - Ching-An Cheng; Han-Pang Huang (IEEE Trans. Cybern., 2016)

Learning the Inverse Dynamics of Robotic Manipulators in Structured Reproducing Kernel Hilbert Space. - Ching-An Cheng; Han-Pang Huang; Huan-Kun Hsu; Wei-Zh Lai; Chih-Chun Cheng (IEEE Trans. Cybern., 2016)

Approximately optimal continuous-time motion planning and control via Probabilistic Inference. - Mustafa Mukadam; Ching-An Cheng; Xinyan Yan; Byron Boots (ICRA, 2017)

Variational Inference for Gaussian Process Models with Linear Complexity. - Ching-An Cheng; Byron Boots (NIPS, 2017)

Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning. - Yunpeng Pan; Ching-An Cheng; Kamil Saigol; Keuntaek Lee; Xinyan Yan; Evangelos A. Theodorou; Byron Boots (CoRR, 2017)

Convergence of Value Aggregation for Imitation Learning. - Ching-An Cheng; Byron Boots (AISTATS, 2018)

Optical Sensing and Control Methods for Soft Pneumatically Actuated Robotic Manipulators. - Jennifer L. Molnar; Ching-An Cheng; Lucas O. Tiziani; Byron Boots; Frank L. Hammond (ICRA, 2018)

Orthogonally Decoupled Variational Gaussian Processes. - Hugh Salimbeni; Ching-An Cheng; Byron Boots; Marc Peter Deisenroth (NeurIPS, 2018)

Agile Autonomous Driving using End-to-End Deep Imitation Learning. - Yunpeng Pan; Ching-An Cheng; Kamil Saigol; Keuntaek Lee; Xinyan Yan; Evangelos A. Theodorou; Byron Boots (Robotics: Science and Systems, 2018)

Fast Policy Learning through Imitation and Reinforcement. - Ching-An Cheng; Xinyan Yan; Nolan Wagener; Byron Boots (UAI, 2018)

RMPflow: A Computational Graph for Automatic Motion Policy Generation. - Ching-An Cheng; Mustafa Mukadam; Jan Issac; Stan Birchfield; Dieter Fox; Byron Boots; Nathan D. Ratliff (WAFR, 2018)

Model-Based Imitation Learning with Accelerated Convergence. - Ching-An Cheng; Xinyan Yan; Evangelos A. Theodorou; Byron Boots (CoRR, 2018)

RMPflow: A Computational Graph for Automatic Motion Policy Generation. - Ching-An Cheng; Mustafa Mukadam; Jan Issac; Stan Birchfield; Dieter Fox; Byron Boots; Nathan D. Ratliff (CoRR, 2018)

Accelerating Imitation Learning with Predictive Models. - Ching-An Cheng; Xinyan Yan; Evangelos A. Theodorou; Byron Boots (AISTATS, 2019)

Truncated Back-propagation for Bilevel Optimization. - Amirreza Shaban; Ching-An Cheng; Nathan Hatch; Byron Boots (AISTATS, 2019)

Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks. - Anqi Li 001; Ching-An Cheng; Byron Boots; Magnus Egerstedt (CDC, 2019)

Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods. - Ching-An Cheng; Xinyan Yan; Byron Boots (CoRL, 2019)

Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping. - Mustafa Mukadam; Ching-An Cheng; Dieter Fox; Byron Boots; Nathan D. Ratliff (CoRL, 2019)

Predictor-Corrector Policy Optimization. - Ching-An Cheng; Xinyan Yan; Nathan D. Ratliff; Byron Boots (ICML, 2019)

An Online Learning Approach to Model Predictive Control. - Nolan Wagener; Ching-An Cheng; Jacob Sacks; Byron Boots (Robotics: Science and Systems, 2019)

Continuous Online Learning and New Insights to Online Imitation Learning. - Jonathan Lee 002; Ching-An Cheng; Ken Goldberg; Byron Boots (CoRR, 2019)

A Reduction from Reinforcement Learning to No-Regret Online Learning. - Ching-An Cheng; Remi Tachet des Combes; Byron Boots; Geoffrey J. Gordon (AISTATS, 2020)

Online Learning with Continuous Variations: Dynamic Regret and Reductions. - Ching-An Cheng; Jonathan Lee 002; Ken Goldberg; Byron Boots (AISTATS, 2020)

Extending Riemmanian Motion Policies to a Class of Underactuated Wheeled-Inverted-Pendulum Robots. - Bruce Wingo; Ching-An Cheng; Muhammad Ali Murtaza; Munzir Zafar; Seth Hutchinson 001 (ICRA, 2020)

Policy Improvement via Imitation of Multiple Oracles. - Ching-An Cheng; Andrey Kolobov; Alekh Agarwal (NeurIPS, 2020)

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks. - Amir Rahimi; Amirreza Shaban; Ching-An Cheng; Richard Hartley 001; Byron Boots (NeurIPS, 2020)

Policy Improvement from Multiple Experts. - Ching-An Cheng; Andrey Kolobov; Alekh Agarwal (CoRR, 2020)

Explaining Fast Improvement in Online Policy Optimization. - Xinyan Yan; Byron Boots; Ching-An Cheng (CoRR, 2020)

RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies. - Ching-An Cheng; Mustafa Mukadam; Jan Issac; Stan Birchfield; Dieter Fox; Byron Boots; Nathan D. Ratliff (CoRR, 2020)

Imitation learning for agile autonomous driving. - Yunpeng Pan; Ching-An Cheng; Kamil Saigol; Keuntaek Lee; Xinyan Yan; Evangelos A. Theodorou; Byron Boots (Int. J. Robotics Res., 2020)

Efficient and principled robot learning: Theory and algorithms. - Ching-An Cheng (2020)

Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. - Andrea Zanette; Ching-An Cheng; Alekh Agarwal (COLT, 2021)

Safe Reinforcement Learning Using Advantage-Based Intervention. - Nolan Wagener; Byron Boots; Ching-An Cheng (ICML, 2021)

Heuristic-Guided Reinforcement Learning. - Ching-An Cheng; Andrey Kolobov; Adith Swaminathan (NeurIPS, 2021)

Bellman-consistent Pessimism for Offline Reinforcement Learning. - Tengyang Xie; Ching-An Cheng; Nan Jiang 008; Paul Mineiro; Alekh Agarwal (NeurIPS, 2021)

RMP2: A Structured Composable Policy Class for Robot Learning. - Anqi Li 001; Ching-An Cheng; Muhammad Asif Rana; Man Xie; Karl Van Wyk; Nathan D. Ratliff; Byron Boots (Robotics: Science and Systems, 2021)

Explaining fast improvement in online imitation learning. - Xinyan Yan; Byron Boots; Ching-An Cheng (UAI, 2021)

RMPflow: A Geometric Framework for Generation of Multitask Motion Policies. - Ching-An Cheng; Mustafa Mukadam; Jan Issac; Stan Birchfield; Dieter Fox; Byron Boots; Nathan D. Ratliff (IEEE Trans Autom. Sci. Eng., 2021)

Adversarially Trained Actor Critic for Offline Reinforcement Learning. - Ching-An Cheng; Tengyang Xie; Nan Jiang 008; Alekh Agarwal (ICML, 2022)

MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control. - Nolan Wagener; Andrey Kolobov; Felipe Vieira Frujeri; Ricky Loynd; Ching-An Cheng; Matthew J. Hausknecht (NeurIPS, 2022)

Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation. - Sanae Amani; Lin F. Yang; Ching-An Cheng (CoRR, 2022)

Hindsight Learning for MDPs with Exogenous Inputs. - Sean R. Sinclair; Felipe Frujeri; Ching-An Cheng; Adith Swaminathan (CoRR, 2022)

ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data. - Tengyang Xie; Mohak Bhardwaj; Nan Jiang 008; Ching-An Cheng (CoRR, 2022)

Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control. - Vivek Myers; Andre Wang He; Kuan Fang; Homer Rich Walke; Philippe Hansen-Estruch; Ching-An Cheng; Mihai Jalobeanu; Andrey Kolobov; Anca D. Dragan; Sergey Levine (CoRL, 2023)

PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining. - Garrett Thomas; Ching-An Cheng; Ricky Loynd; Felipe Vieira Frujeri; Vibhav Vineet; Mihai Jalobeanu; Andrey Kolobov (CoRL, 2023)

Provably Efficient Lifelong Reinforcement Learning with Linear Representation. - Sanae Amani; Lin Yang 011; Ching-An Cheng (ICLR, 2023)

MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations. - Anqi Li 001; Byron Boots; Ching-An Cheng (ICML, 2023)

Provable Reset-free Reinforcement Learning by No-Regret Reduction. - Hoai-An Nguyen; Ching-An Cheng (ICML, 2023)

Hindsight Learning for MDPs with Exogenous Inputs. - Sean R. Sinclair; Felipe Vieira Frujeri; Ching-An Cheng; Luke Marshall; Hugo de Oliveira Barbalho; Jingling Li; Jennifer Neville; Ishai Menache; Adith Swaminathan (ICML, 2023)

Adversarial Model for Offline Reinforcement Learning. - Mohak Bhardwaj; Tengyang Xie; Byron Boots; Nan Jiang 008; Ching-An Cheng (NeurIPS, 2023)

Survival Instinct in Offline Reinforcement Learning. - Anqi Li 001; Dipendra Misra; Andrey Kolobov; Ching-An Cheng (NeurIPS, 2023)

PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining. - Garrett Thomas; Ching-An Cheng; Ricky Loynd; Vibhav Vineet; Mihai Jalobeanu; Andrey Kolobov (CoRR, 2023)

Interactive Robot Learning from Verbal Correction. - Huihan Liu; Alice Chen; Yuke Zhu; Adith Swaminathan; Andrey Kolobov; Ching-An Cheng (CoRR, 2023)

LLF-Bench: Benchmark for Interactive Learning from Language Feedback. - Ching-An Cheng; Andrey Kolobov; Dipendra Misra; Allen Nie; Adith Swaminathan (CoRR, 2023)

Improving Offline RL by Blending Heuristics. - Sinong Geng; Aldo Pacchiano; Andrey Kolobov; Ching-An Cheng (ICLR, 2024)

PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control. - Ruijie Zheng; Ching-An Cheng; Hal Daumé III; Furong Huang; Andrey Kolobov (ICML, 2024)

Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs. - Ching-An Cheng; Allen Nie; Adith Swaminathan (NeurIPS, 2024)

How to Solve Contextual Goal-Oriented Problems with Offline Datasets? - Ying Fan; Jingling Li; Adith Swaminathan; Aditya Modi 002; Ching-An Cheng (NeurIPS, 2024)

PRISE: Learning Temporal Action Abstractions as a Sequence Compression Problem. - Ruijie Zheng; Ching-An Cheng; Hal Daumé III; Furong Huang; Andrey Kolobov (CoRR, 2024)

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences. - Corby Rosset; Ching-An Cheng; Arindam Mitra; Michael Santacroce; Ahmed Awadallah 001; Tengyang Xie (CoRR, 2024)

The Importance of Directional Feedback for LLM-based Optimizers. - Allen Nie; Ching-An Cheng; Andrey Kolobov; Adith Swaminathan (CoRR, 2024)

Trace is the New AutoDiff - Unlocking Efficient Optimization of Computational Workflows. - Ching-An Cheng; Allen Nie; Adith Swaminathan (CoRR, 2024)

Streetwise Agents: Empowering Offline RL Policies to Outsmart Exogenous Stochastic Disturbances in RTC. - Aditya Soni; Mayukh Das; Anjaly Parayil; Supriyo Ghosh; Shivam Shandilya; Ching-An Cheng; Vishak Gopal; Sami Khairy; Gabriel Mittag; Yasaman Hosseinkashi; Chetan Bansal (CoRR, 2024)

Rapidly Adapting Policies to the Real-World via Simulation-Guided Fine-Tuning. - Patrick Yin; Tyler Westenbroek; Ching-An Cheng; Andrey Kolobov; Abhishek Gupta (ICLR, 2025)

Provably Learning from Language Feedback. - Wanqiao Xu; Allen Nie; Ruijie Zheng; Aditya Modi 002; Adith Swaminathan; Ching-An Cheng (CoRR, 2025)