5 citations · 17 across the 14 of their papers we have counts for
15 papers
Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations
Xin Liu, Haoran Li, Dongbin Zhao
Humans can efficiently extract knowledge and learn skills from the videos within only a few trials and errors. However, it poses a big challenge to replicate this learning process…
TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning
Yuhui Chen, Haoran Li, Zhennan Jiang +2
Developing scalable and generalizable reward engineering for reinforcement learning (RL) is crucial for creating general-purpose agents, especially in the challenging domain of rob…
ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
Yuhui Chen, Shuai Tian, Shugao Liu +3
Vision-Language-Action (VLA) models have shown substantial potential in real-world robotic manipulation. However, fine-tuning these models through supervised learning struggles to…
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization
Haoran Li, Zhennan Jiang, Yuhui Chen +1
With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training…
SELU: Self-Learning Embodied MLLMs in Unknown Environments
Boyu Li, Haobin Jiang, Ziluo Ding +4
Recently, multimodal large language models (MLLMs) have demonstrated strong visual understanding and decision-making capabilities, enabling the exploration of autonomously improvin…
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning
Xin Liu, Yaran Chen, Dongbin Zhao
In visual Reinforcement Learning (RL), upstream representation learning largely determines the effect of downstream policy learning. Employing auxiliary tasks allows the agent to e…
Advancing Object Goal Navigation Through LLM-enhanced Object Affinities Transfer
Mengying Lin, Shugao Liu, Dingxi Zhang +4
Object-goal navigation requires mobile robots to efficiently locate targets with visual and spatial information, yet existing methods struggle with generalization in unseen environ…
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game
Guangzheng Hu, Yuanheng Zhu, Haoran Li +1
Many real-world applications involve some agents that fall into two teams, with payoffs that are equal within the same team but of opposite sign across the opponent team. The so-ca…
Boosting Continuous Control with Consistency Policy
Yuhui Chen, Haoran Li, Dongbin Zhao
Due to its training stability and strong expression, the diffusion model has attracted considerable attention in offline reinforcement learning. However, several challenges have al…
Multi-modal Learning based Prediction for Disease
Yaran Chen, Xueyu Chen, Yu Han +4
Non alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, which can be predicted accurately to prevent advanced fibrosis and cirrhosis. While, a…
Cross-domain Random Pre-training with Prototypes for Reinforcement Learning
Xin Liu, Yaran Chen, Haoran Li +2
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Unsupervised c…
NeuronsGym: A Hybrid Framework and Benchmark for Robot Tasks with Sim2Real Policy Learning
Haoran Li, Shasha Liu, Mingjun Ma +3
The rise of embodied AI has greatly improved the possibility of general mobile agent systems. At present, many evaluation platforms with rich scenes, high visual fidelity and vario…
MVM3Det: A Novel Method for Multi-view Monocular 3D Detection
Li Haoran, Duan Zicheng, Ma Mingjun +3
Monocular 3D object detection encounters occlusion problems in many application scenarios, such as traffic monitoring, pedestrian monitoring, etc., which leads to serious false neg…
Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environment
Haoran Li, Qichao Zhang, Dongbin Zhao
This paper investigates the automatic exploration problem under the unknown environment, which is the key point of applying the robotic system to some social tasks. The solution to…
BiFNet: Bidirectional Fusion Network for Road Segmentation
Haoran Li, Yaran Chen, Qichao Zhang +1
Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is ma…