1 citations · 1 across the 10 of their papers we have counts for
5 papers · 1 filter
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…