19 citations · 39 across the 13 of their papers we have counts for
14 papers
MIRROR: Reflecting Hidden Symmetries from Offline Robot Interaction Data
Shiqi Liu, Yihang Yao, Peide Huang +2
Symmetry-aware robot policy learning requires coupled observation-action transformations. For pixel observations, even simple physical symmetries can induce complex, viewpoint-depe…
Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications
Xilun Zhang, Shiqi Liu, Peide Huang +4
Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies between simulated and real-world dynamics. Traditional methods like Domain Randomization o…
CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories
Peide Huang, Wenhao Ding, Benjamin Stoler +3
Simulation is an indispensable tool in the development and testing of autonomous vehicles (AVs), offering an efficient and safe alternative to road testing. An outstanding challeng…
Gradient Shaping for Multi-Constraint Safe Reinforcement Learning
Yihang Yao, Zuxin Liu, Zhepeng Cen +4
Online safe reinforcement learning (RL) involves training a policy that maximizes task efficiency while satisfying constraints via interacting with the environments. In this paper,…
Creative Robot Tool Use with Large Language Models
Mengdi Xu, Peide Huang, Wenhao Yu +7
Tool use is a hallmark of advanced intelligence, exemplified in both animal behavior and robotic capabilities. This paper investigates the feasibility of imbuing robots with the ab…
What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Peide Huang, Xilun Zhang, Ziang Cao +6
Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inev…
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging
Jielin Qiu, Peide Huang, Makiya Nakashima +10
Self-supervised learning is crucial for clinical imaging applications, given the lack of explicit labels in healthcare. However, conventional approaches that rely on precise vision…
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mengdi Xu, Peide Huang, Yaru Niu +8
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators. Robust RL has been applied to deal with task ambiguity, but may result i…
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Peide Huang, Mengdi Xu, Jiacheng Zhu +3
Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks. In this work, we focus on the id…
Continual Vision-based Reinforcement Learning with Group Symmetries
Shiqi Liu, Mengdi Xu, Piede Huang +3
Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new po…
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
Mengdi Xu, Zuxin Liu, Peide Huang +4
A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} cons…
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training
Peide Huang, Mengdi Xu, Fei Fang +1
Robust Reinforcement Learning (RL) focuses on improving performances under model errors or adversarial attacks, which facilitates the real-life deployment of RL agents. Robust Adve…
Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
Jielin Qiu, Jiacheng Zhu, Mengdi Xu +5
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart dise…
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Mengdi Xu, Peide Huang, Fengpei Li +6
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces wher…