1 citations · 1 across the 10 of their papers we have counts for
11 papers
Posterior Optimization with Clipped Objective for Bridging Efficiency and Stability in Generative Policy Learning
Yuhui Chen, Haoran Li, Zhennan Jiang +4
Expressive generative models have advanced robotic manipulation by capturing complex, multi-modal action distributions over temporally extended trajectories. However, fine-tuning t…
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
Xin Liu, Yixuan Li, Yuhui Chen +3
Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human jud…
WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
Zhennan Jiang, Shangqing Zhou, Yutong Jiang +11
Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interact…
Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning
Yuan Liu, Haoran Li, Shuai Tian +5
Pretrained on large-scale and diverse datasets, VLA models demonstrate strong generalization and adaptability as general-purpose robotic policies. However, Supervised Fine-Tuning (…
QDepth-VLA: Quantized Depth Prediction as Auxiliary Supervision for Vision-Language-Action Models
Yixuan Li, Yuhui Chen, Mingcai Zhou +3
Spatial perception and reasoning are crucial for Vision-Language-Action (VLA) models to accomplish fine-grained manipulation tasks. However, existing approaches often lack the abil…
Survey of Vision-Language-Action Models for Embodied Manipulation
Haoran Li, Yuhui Chen, Wenbo Cui +5
Embodied intelligence systems, which enhance agent capabilities through continuous environment interactions, have garnered significant attention from both academia and industry. Vi…
CLAR: Learning 3D Representations for Robotic Manipulation by Fusing Masked Reconstruction with Multi-Level Contrastive Alignment
Wenbo Cui, Chengyang Zhao, Yuhui Chen +4
The spatial information inherent in 3D point clouds is crucial for robotic manipulation. However, existing 3D pre-training methods face a fundamental trade-off: Masked Autoencoding…
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…
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…