59 citations · 454 across the 124 of their papers we have counts for
24 papers · 1 filter
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,…
RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
Wenhao Ding, Yulong Cao, Ding Zhao +2
Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing. Although significant progress has bee…
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
Haohong Lin, Wenhao Ding, Zuxin Liu +4
In the domain of autonomous driving, the offline Reinforcement Learning~(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline data…
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…
COMPOSER: Scalable and Robust Modular Policies for Snake Robots
Yuyou Zhang, Yaru Niu, Xingyu Liu +1
Snake robots have showcased remarkable compliance and adaptability in their interaction with environments, mirroring the traits of their natural counterparts. While their hyper-red…
Reinforcement Learning in a Safety-Embedded MDP with Trajectory Optimization
Fan Yang, Wenxuan Zhou, Zuxin Liu +2
Safe Reinforcement Learning (RL) plays an important role in applying RL algorithms to safety-critical real-world applications, addressing the trade-off between maximizing rewards a…
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
Zuxin Liu, Jesse Zhang, Kavosh Asadi +4
The full potential of large pretrained models remains largely untapped in control domains like robotics. This is mainly because of the scarcity of data and the computational challe…
Influence of Camera-LiDAR Configuration on 3D Object Detection for Autonomous Driving
Ye Li, Hanjiang Hu, Zuxin Liu +3
Cameras and LiDARs are both important sensors for autonomous driving, playing critical roles in 3D object detection. Camera-LiDAR Fusion has been a prevalent solution for robust an…
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning
Yihang Yao, Zuxin Liu, Zhepeng Cen +4
Safe reinforcement learning (RL) focuses on training reward-maximizing agents subject to pre-defined safety constraints. Yet, learning versatile safe policies that can adapt to var…
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie +39
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical a…
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Wenhao Ding, Laixi Shi, Yuejie Chi +1
Robustness has been extensively studied in reinforcement learning (RL) to handle various forms of uncertainty such as random perturbations, rare events, and malicious attacks. In t…
Datasets and Benchmarks for Offline Safe Reinforcement Learning
Zuxin Liu, Zijian Guo, Haohong Lin +8
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation…
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…
Privacy Risks in Reinforcement Learning for Household Robots
Miao Li, Wenhao Ding, Ding Zhao
The prominence of embodied Artificial Intelligence (AI), which empowers robots to navigate, perceive, and engage within virtual environments, has attracted significant attention, o…
MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videos
Jielin Qiu, Jiacheng Zhu, William Han +9
Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless, numerous limitations exist within existing public MSMO datasets,…
Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models
Wenhao Ding, Tong Che, Ding Zhao +1
Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature. However, we will show that current RCRL…
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…
Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report
Jielin Qiu, Jiacheng Zhu, Shiqi Liu +7
Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies. Despite the growing interest, most…
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
Yaru Niu, Shiyu Jin, Zeqing Zhang +3
In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning. This task is particularly challenging due to the complex dynam…
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…
Constrained Decision Transformer for Offline Safe Reinforcement Learning
Zuxin Liu, Zijian Guo, Yihang Yao +4
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy fr…
Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
Jiacheng Zhu, Jielin Qiu, Aritra Guha +4
We propose to study and promote the robustness of a model as per its performance through the interpolation of training data distributions. Specifically, (1) we augment the data by…
Learning to View: Decision Transformers for Active Object Detection
Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande +4
Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environm…