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2020 – today
- 2024
[c44]Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng:
Improving Offline RL by Blending Heuristics. ICLR 2024- [c43]Ruijie Zheng, Ching-An Cheng, Hal Daumé III, Furong Huang, Andrey Kolobov:
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control. ICML 2024 - [i44]Ruijie Zheng, Ching-An Cheng, Hal Daumé III, Furong Huang, Andrey Kolobov:
PRISE: Learning Temporal Action Abstractions as a Sequence Compression Problem. CoRR abs/2402.10450 (2024) - [i43]Corby Rosset, Ching-An Cheng, Arindam Mitra, Michael Santacroce, Ahmed Awadallah, Tengyang Xie:
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences. CoRR abs/2404.03715 (2024) - [i42]Allen Nie, Ching-An Cheng, Andrey Kolobov, Adith Swaminathan:
The Importance of Directional Feedback for LLM-based Optimizers. CoRR abs/2405.16434 (2024) - [i41]Ching-An Cheng, Allen Nie, Adith Swaminathan:
Trace is the New AutoDiff - Unlocking Efficient Optimization of Computational Workflows. CoRR abs/2406.16218 (2024) - 2023
- [c42]Garrett Thomas, Ching-An Cheng, Ricky Loynd, Felipe Vieira Frujeri, Vibhav Vineet, Mihai Jalobeanu, Andrey Kolobov:
PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining. CoRL 2023: 2624-2641 - [c41]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:
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control. CoRL 2023: 3894-3908 - [c40]Sanae Amani, Lin Yang, Ching-An Cheng:
Provably Efficient Lifelong Reinforcement Learning with Linear Representation. ICLR 2023 - [c39]Anqi Li, Byron Boots, Ching-An Cheng:
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations. ICML 2023: 19360-19384 - [c38]Hoai-An Nguyen, Ching-An Cheng:
Provable Reset-free Reinforcement Learning by No-Regret Reduction. ICML 2023: 25939-25955 - [c37]Sean R. Sinclair, Felipe Vieira Frujeri, Ching-An Cheng, Luke Marshall, Hugo de Oliveira Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan:
Hindsight Learning for MDPs with Exogenous Inputs. ICML 2023: 31877-31914 - [c36]Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng:
Adversarial Model for Offline Reinforcement Learning. NeurIPS 2023 - [c35]Anqi Li, Dipendra Misra, Andrey Kolobov, Ching-An Cheng:
Survival Instinct in Offline Reinforcement Learning. NeurIPS 2023 - [i40]Hoai-An Nguyen, Ching-An Cheng:
Provable Reset-free Reinforcement Learning by No-Regret Reduction. CoRR abs/2301.02389 (2023) - [i39]Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng:
Adversarial Model for Offline Reinforcement Learning. CoRR abs/2302.11048 (2023) - [i38]Garrett Thomas, Ching-An Cheng, Ricky Loynd, Vibhav Vineet, Mihai Jalobeanu, Andrey Kolobov:
PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining. CoRR abs/2303.08789 (2023) - [i37]Anqi Li, Byron Boots, Ching-An Cheng:
MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations. CoRR abs/2303.17156 (2023) - [i36]Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng:
Improving Offline RL by Blending Heuristics. CoRR abs/2306.00321 (2023) - [i35]Anqi Li, Dipendra Misra, Andrey Kolobov, Ching-An Cheng:
Survival Instinct in Offline Reinforcement Learning. CoRR abs/2306.03286 (2023) - [i34]Vivek Myers, Andre He, Kuan Fang, Homer Walke, Philippe Hansen-Estruch, Ching-An Cheng, Mihai Jalobeanu, Andrey Kolobov, Anca D. Dragan, Sergey Levine:
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control. CoRR abs/2307.00117 (2023) - [i33]Huihan Liu, Alice Chen, Yuke Zhu, Adith Swaminathan, Andrey Kolobov, Ching-An Cheng:
Interactive Robot Learning from Verbal Correction. CoRR abs/2310.17555 (2023) - [i32]Ching-An Cheng, Andrey Kolobov, Dipendra Misra, Allen Nie, Adith Swaminathan:
LLF-Bench: Benchmark for Interactive Learning from Language Feedback. CoRR abs/2312.06853 (2023) - 2022
- [c34]Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. ICML 2022: 3852-3878 - [c33]Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri, Ricky Loynd, Ching-An Cheng, Matthew J. Hausknecht:
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control. NeurIPS 2022 - [i31]Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. CoRR abs/2202.02446 (2022) - [i30]Sanae Amani, Lin F. Yang
, Ching-An Cheng:
Provably Efficient Lifelong Reinforcement Learning with Linear Function Approximation. CoRR abs/2206.00270 (2022) - [i29]Sean R. Sinclair, Felipe Frujeri, Ching-An Cheng, Adith Swaminathan:
Hindsight Learning for MDPs with Exogenous Inputs. CoRR abs/2207.06272 (2022) - [i28]Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri, Ricky Loynd, Ching-An Cheng, Matthew J. Hausknecht:
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control. CoRR abs/2208.07363 (2022) - [i27]Tengyang Xie, Mohak Bhardwaj, Nan Jiang, Ching-An Cheng:
ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data. CoRR abs/2211.04538 (2022) - 2021
- [j5]Ching-An Cheng, Mustafa Mukadam, Jan Issac, Stan Birchfield, Dieter Fox, Byron Boots, Nathan D. Ratliff:
RMPflow: A Geometric Framework for Generation of Multitask Motion Policies. IEEE Trans Autom. Sci. Eng. 18(3): 968-987 (2021) - [c32]Andrea Zanette, Ching-An Cheng, Alekh Agarwal:
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. COLT 2021: 4473-4525 - [c31]Nolan Wagener, Byron Boots, Ching-An Cheng:
Safe Reinforcement Learning Using Advantage-Based Intervention. ICML 2021: 10630-10640 - [c30]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS 2021: 6683-6694 - [c29]Ching-An Cheng, Andrey Kolobov, Adith Swaminathan:
Heuristic-Guided Reinforcement Learning. NeurIPS 2021: 13550-13563 - [c28]Anqi Li, Ching-An Cheng, Muhammad Asif Rana, Man Xie, Karl Van Wyk, Nathan D. Ratliff, Byron Boots:
RMP2: A Structured Composable Policy Class for Robot Learning. Robotics: Science and Systems 2021 - [c27]Xinyan Yan, Byron Boots, Ching-An Cheng:
Explaining fast improvement in online imitation learning. UAI 2021: 1874-1884 - [i26]Anqi Li, Ching-An Cheng, Muhammad Asif Rana, Man Xie, Karl Van Wyk, Nathan D. Ratliff, Byron Boots:
RMP2: A Structured Composable Policy Class for Robot Learning. CoRR abs/2103.05922 (2021) - [i25]Andrea Zanette, Ching-An Cheng, Alekh Agarwal:
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation. CoRR abs/2103.12923 (2021) - [i24]Ching-An Cheng, Andrey Kolobov, Adith Swaminathan:
Heuristic-Guided Reinforcement Learning. CoRR abs/2106.02757 (2021) - [i23]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. CoRR abs/2106.06926 (2021) - [i22]Nolan Wagener, Byron Boots, Ching-An Cheng:
Safe Reinforcement Learning Using Advantage-Based Intervention. CoRR abs/2106.09110 (2021) - 2020
- [j4]Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos A. Theodorou, Byron Boots:
Imitation learning for agile autonomous driving. Int. J. Robotics Res. 39(2-3) (2020) - [c26]Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots:
Online Learning with Continuous Variations: Dynamic Regret and Reductions. AISTATS 2020: 2218-2228 - [c25]Ching-An Cheng, Remi Tachet des Combes, Byron Boots, Geoffrey J. Gordon:
A Reduction from Reinforcement Learning to No-Regret Online Learning. AISTATS 2020: 3514-3524 - [c24]Bruce Wingo, Ching-An Cheng, Muhammad Ali Murtaza, Munzir Zafar, Seth Hutchinson:
Extending Riemmanian Motion Policies to a Class of Underactuated Wheeled-Inverted-Pendulum Robots. ICRA 2020: 3967-3973 - [c23]Ching-An Cheng, Andrey Kolobov, Alekh Agarwal:
Policy Improvement via Imitation of Multiple Oracles. NeurIPS 2020 - [c22]Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, Byron Boots:
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks. NeurIPS 2020 - [i21]Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Byron Boots, Richard Hartley:
Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks. CoRR abs/2003.06820 (2020) - [i20]Ching-An Cheng, Andrey Kolobov, Alekh Agarwal:
Policy Improvement from Multiple Experts. CoRR abs/2007.00795 (2020) - [i19]Xinyan Yan, Byron Boots, Ching-An Cheng:
Explaining Fast Improvement in Online Policy Optimization. CoRR abs/2007.02520 (2020) - [i18]Ching-An Cheng, Mustafa Mukadam, Jan Issac, Stan Birchfield, Dieter Fox, Byron Boots, Nathan D. Ratliff:
RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies. CoRR abs/2007.14256 (2020)
2010 – 2019
- 2019
- [c21]Amirreza Shaban, Ching-An Cheng, Nathan Hatch, Byron Boots:
Truncated Back-propagation for Bilevel Optimization. AISTATS 2019: 1723-1732 - [c20]Ching-An Cheng, Xinyan Yan, Evangelos A. Theodorou, Byron Boots:
Accelerating Imitation Learning with Predictive Models. AISTATS 2019: 3187-3196 - [c19]Anqi Li, Ching-An Cheng, Byron Boots, Magnus Egerstedt:
Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks. CDC 2019: 1144-1151 - [c18]Mustafa Mukadam, Ching-An Cheng, Dieter Fox, Byron Boots, Nathan D. Ratliff:
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping. CoRL 2019: 204-219 - [c17]Ching-An Cheng, Xinyan Yan, Byron Boots:
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods. CoRL 2019: 1379-1394 - [c16]Ching-An Cheng, Xinyan Yan, Nathan D. Ratliff, Byron Boots:
Predictor-Corrector Policy Optimization. ICML 2019: 1151-1161 - [c15]Nolan Wagener, Ching-An Cheng, Jacob Sacks, Byron Boots:
An Online Learning Approach to Model Predictive Control. Robotics: Science and Systems 2019 - [i17]Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots:
Online Learning with Continuous Variations: Dynamic Regret and Reductions. CoRR abs/1902.07286 (2019) - [i16]Nolan Wagener, Ching-An Cheng, Jacob Sacks, Byron Boots:
An Online Learning Approach to Model Predictive Control. CoRR abs/1902.08967 (2019) - [i15]Anqi Li, Ching-An Cheng, Byron Boots, Magnus Egerstedt:
Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks. CoRR abs/1903.12605 (2019) - [i14]Ching-An Cheng, Xinyan Yan, Byron Boots:
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods. CoRR abs/1908.03263 (2019) - [i13]Mustafa Mukadam, Ching-An Cheng, Dieter Fox, Byron Boots, Nathan D. Ratliff:
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping. CoRR abs/1910.02646 (2019) - [i12]Ching-An Cheng, Remi Tachet des Combes, Byron Boots, Geoffrey J. Gordon:
A Reduction from Reinforcement Learning to No-Regret Online Learning. CoRR abs/1911.05873 (2019) - [i11]Jonathan Lee, Ching-An Cheng, Ken Goldberg, Byron Boots:
Continuous Online Learning and New Insights to Online Imitation Learning. CoRR abs/1912.01261 (2019) - 2018
- [c14]Ching-An Cheng, Byron Boots:
Convergence of Value Aggregation for Imitation Learning. AISTATS 2018: 1801-1809 - [c13]Jennifer L. Molnar, Ching-An Cheng, Lucas O. Tiziani, Byron Boots, Frank L. Hammond:
Optical Sensing and Control Methods for Soft Pneumatically Actuated Robotic Manipulators. ICRA 2018: 1-8 - [c12]Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Peter Deisenroth:
Orthogonally Decoupled Variational Gaussian Processes. NeurIPS 2018: 8725-8734 - [c11]Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos A. Theodorou, Byron Boots:
Agile Autonomous Driving using End-to-End Deep Imitation Learning. Robotics: Science and Systems 2018 - [c10]Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots:
Fast Policy Learning through Imitation and Reinforcement. UAI 2018: 845-855 - [c9]Ching-An Cheng, Mustafa Mukadam, Jan Issac, Stan Birchfield, Dieter Fox, Byron Boots, Nathan D. Ratliff:
RMPflow: A Computational Graph for Automatic Motion Policy Generation. WAFR 2018: 441-457 - [i10]Ching-An Cheng, Byron Boots:
Convergence of Value Aggregation for Imitation Learning. CoRR abs/1801.07292 (2018) - [i9]Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots:
Fast Policy Learning through Imitation and Reinforcement. CoRR abs/1805.10413 (2018) - [i8]Ching-An Cheng, Xinyan Yan, Evangelos A. Theodorou, Byron Boots:
Model-Based Imitation Learning with Accelerated Convergence. CoRR abs/1806.04642 (2018) - [i7]Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Peter Deisenroth:
Orthogonally Decoupled Variational Gaussian Processes. CoRR abs/1809.08820 (2018) - [i6]Ching-An Cheng, Xinyan Yan, Nathan D. Ratliff, Byron Boots:
Predictor-Corrector Policy Optimization. CoRR abs/1810.06509 (2018) - [i5]Amirreza Shaban, Ching-An Cheng, Nathan Hatch, Byron Boots:
Truncated Back-propagation for Bilevel Optimization. CoRR abs/1810.10667 (2018) - [i4]Ching-An Cheng, Mustafa Mukadam, Jan Issac, Stan Birchfield, Dieter Fox, Byron Boots, Nathan D. Ratliff:
RMPflow: A Computational Graph for Automatic Motion Policy Generation. CoRR abs/1811.07049 (2018) - 2017
- [c8]Mustafa Mukadam, Ching-An Cheng, Xinyan Yan, Byron Boots:
Approximately optimal continuous-time motion planning and control via Probabilistic Inference. ICRA 2017: 664-671 - [c7]Ching-An Cheng, Byron Boots:
Variational Inference for Gaussian Process Models with Linear Complexity. NIPS 2017: 5184-5194 - [i3]Mustafa Mukadam, Ching-An Cheng, Xinyan Yan, Byron Boots:
Approximately Optimal Continuous-Time Motion Planning and Control via Probabilistic Inference. CoRR abs/1702.07335 (2017) - [i2]Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos A. Theodorou, Byron Boots:
Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning. CoRR abs/1709.07174 (2017) - [i1]Ching-An Cheng, Byron Boots:
Variational Inference for Gaussian Process Models with Linear Complexity. CoRR abs/1711.10127 (2017) - 2016
- [j3]Sheng-Yen Lo, Ching-An Cheng, Han-Pang Huang:
Virtual Impedance Control for Safe Human-Robot Interaction. J. Intell. Robotic Syst. 82(1): 3-19 (2016) - [j2]Ching-An Cheng, Han-Pang Huang, Huan-Kun Hsu, Wei-Zh Lai, Chih-Chun Cheng:
Learning the Inverse Dynamics of Robotic Manipulators in Structured Reproducing Kernel Hilbert Space. IEEE Trans. Cybern. 46(7): 1691-1703 (2016) - [j1]Ching-An Cheng, Han-Pang Huang:
Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems. IEEE Trans. Cybern. 46(12): 3247-3258 (2016) - [c6]Ching-An Cheng, Byron Boots:
Incremental Variational Sparse Gaussian Process Regression. NIPS 2016: 4403-4411 - 2015
- [c5]Che-Hsuan Chang, Han-Pang Huang, Huan-Kun Hsu, Ching-An Cheng:
Humanoid robot push-recovery strategy based on CMP criterion and angular momentum regulation. AIM 2015: 761-766 - [c4]Ming-Bao Huang, Han-Pang Huang, Chih-Chun Cheng, Ching-An Cheng:
Efficient grasp synthesis and control strategy for robot hand-arm system. CASE 2015: 1256-1257 - 2013
- [c3]Ching-An Cheng, Tzu-Hao Huang, Han-Pang Huang:
Bayesian human intention estimator for exoskeleton system. AIM 2013: 465-470 - [c2]Tzu-Hao Huang, Ching-An Cheng, Han-Pang Huang:
Self-learning assistive exoskeleton with sliding mode admittance control. IROS 2013: 698-703 - 2012
- [c1]Tzu-Hao Huang, Han-Pang Huang, Ching-An Cheng, Jiun-Yih Kuan, Po-Ting Lee, Shih-Yi Huang:
Design of a new hybrid control and knee orthosis for human walking and rehabilitation. IROS 2012: 3653-3658
Coauthor Index
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