19 citations · 46 across the 16 of their papers we have counts for
17 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…
MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning
Yiqing Liang, Jielin Qiu, Wenhao Ding +7
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a powerful paradigm for post-training large language models (LLMs), achieving state-of-the-art perform…
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…
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…
Guardians as You Fall: Active Mode Transition for Safe Falling
Yikai Wang, Mengdi Xu, Guanya Shi +1
Recent advancements in optimal control and reinforcement learning have enabled quadrupedal robots to perform various agile locomotion tasks over diverse terrains. During these agil…
Embodied Executable Policy Learning with Language-based Scene Summarization
Jielin Qiu, Mengdi Xu, William Han +2
Large Language models (LLMs) have shown remarkable success in assisting robot learning tasks, i.e., complex household planning. However, the performance of pretrained LLMs heavily…
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…
Hyper-Decision Transformer for Efficient Online Policy Adaptation
Mengdi Xu, Yuchen Lu, Yikang Shen +3
Decision Transformers (DT) have demonstrated strong performances in offline reinforcement learning settings, but quickly adapting to unseen novel tasks remains challenging. To addr…
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
Jielin Qiu, William Han, Jiacheng Zhu +5
Recent advancements in Large Language Models (LLMs) have drawn increasing attention since the learned embeddings pretrained on large-scale datasets have shown powerful ability in v…
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…
Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment
Jielin Qiu, Jiacheng Zhu, Mengdi Xu +6
Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding. It plays an essential role in real-world applications, i.e., automa…
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…
Can Brain Signals Reveal Inner Alignment with Human Languages?
William Han, Jielin Qiu, Jiacheng Zhu +4
Brain Signals, such as Electroencephalography (EEG), and human languages have been widely explored independently for many downstream tasks, however, the connection between them has…
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…