1.1k citations · 1.3k across the 29 of their papers we have counts for
30 papers
Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
Yihang Yao, Zhepeng Cen, Haohong Lin +6
Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following a…
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…
Behavior Injection: Preparing Language Models for Reinforcement Learning
Zhepeng Cen, Yihang Yao, William Han +2
Reinforcement learning (RL) has emerged as a powerful post-training technique to incentivize the reasoning ability of large language models (LLMs). However, LLMs can respond very i…
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training
Yihang Yao, Zhepeng Cen, Miao Li +6
Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. However, even minor variations in query phrasing, despite preserving the underlyi…
Feasibility Consistent Representation Learning for Safe Reinforcement Learning
Zhepeng Cen, Yihang Yao, Zuxin Liu +1
In the field of safe reinforcement learning (RL), finding a balance between satisfying safety constraints and optimizing reward performance presents a significant challenge. A key…
Learning from Sparse Offline Datasets via Conservative Density Estimation
Zhepeng Cen, Zuxin Liu, Zitong Wang +3
Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment. Howe…
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,…
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…
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…
Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations
Hanjiang Hu, Zuxin Liu, Linyi Li +2
Deep learning-based visual perception models lack robustness when faced with camera motion perturbations in practice. The current certification process for assessing robustness is…
Learning Shared Safety Constraints from Multi-task Demonstrations
Konwoo Kim, Gokul Swamy, Zuxin Liu +3
Regardless of the particular task we want them to perform in an environment, there are often shared safety constraints we want our agents to respect. For example, regardless of whe…
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…
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…
Robustness Certification of Visual Perception Models via Camera Motion Smoothing
Hanjiang Hu, Zuxin Liu, Linyi Li +2
A vast literature shows that the learning-based visual perception model is sensitive to adversarial noises, but few works consider the robustness of robotic perception models under…
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…
SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles
Chejian Xu, Wenhao Ding, Weijie Lyu +6
As shown by recent studies, machine intelligence-enabled systems are vulnerable to test cases resulting from either adversarial manipulation or natural distribution shifts. This ha…
On the Robustness of Safe Reinforcement Learning under Observational Perturbations
Zuxin Liu, Zijian Guo, Zhepeng Cen +4
Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find tha…
Constrained Variational Policy Optimization for Safe Reinforcement Learning
Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev +4
Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before deploying them to safety-critical applications. Previous primal-dual style approache…
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving
Hanjiang Hu, Zuxin Liu, Sharad Chitlangia +2
The past few years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles. While most of the existing works focus on develop…
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
Baiming Chen, Zuxin Liu, Jiacheng Zhu +3
Safety is a critical concern when deploying reinforcement learning agents for realistic tasks. Recently, safe reinforcement learning algorithms have been developed to optimize the…
SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments
Hanjiang Hu, Baoquan Yang, Zhijian Qiao +6
Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving, and the generalization of learning-based algorithms on diffe…