2 citations · 3 across the 13 of their papers we have counts for
14 papers
ReTouch: Empowering Contact-Rich Dexterous Manipulation with Online-Refined Tactile Prediction
Shiqi Zhang, Xin Zhang, Yedong Shen +9
Fusing tactile signals has proven effective for contact-rich manipulation, enabling robots to perceive contact states and adapt to rapidly changing physical interactions. Yet effec…
Scaling Native Multimodal Pre-Training From Scratch
Haoyuan Wu, Aoqi Wu, Hai Wang +3
Although large language models (LLMs) exhibit remarkable reasoning capabilities, their reliance on text-only pre-training restricts the perception of the multimodal physical world.…
iFLYTEK-Embodied-Omni Technical Report
Yuan Zhang, Jingfei Ni, Guanchen Lu +12
General-purpose embodied agents must understand multimodal instructions, anticipate how their environment will evolve, and produce precise control actions over extended horizons. E…
GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation
Yuan Zhang, Shiqi Zhang, Yedong Shen +11
Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot emb…
Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Mao Zheng, Zheng Li, Tao Chen +10
Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of wh…
Drift-Based Policy Optimization: Native One-Step Policy Learning for Online Robot Control
Yuxuan Gao, Yedong Shen, Shiqi Zhang +6
Diffusion policies effectively model multimodal action distributions for robotic manipulation, but their iterative denoising requires tens to hundreds of network function evaluatio…
Diversity or Precision? A Deep Dive into Next Token Prediction
Haoyuan Wu, Hai Wang, Jiajia Wu +5
Recent advancements have shown that reinforcement learning (RL) can substantially improve the reasoning abilities of large language models (LLMs). The effectiveness of such RL trai…
iFlyBot-VLM Technical Report
Xin Nie, Zhiyuan Cheng, Yuan Zhang +4
We introduce iFlyBot-VLM, a general-purpose Vision-Language Model (VLM) used to improve the domain of Embodied Intelligence. The central objective of iFlyBot-VLM is to bridge the c…
iFlyBot-VLA Technical Report
Yuan Zhang, Chenyu Xue, Wenjie Xu +3
We introduce iFlyBot-VLA, a large-scale Vision-Language-Action (VLA) model trained under a novel framework. The main contributions are listed as follows: (1) a latent action model…
Reinforcement Learning on Pre-Training Data
Siheng Li, Kejiao Li, Zenan Xu +33
The growing disparity between the exponential scaling of computational resources and the finite growth of high-quality text data now constrains conventional scaling approaches for…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
Multimodal Tree Decoder for Table of Contents Extraction in Document Images
Pengfei Hu, Zhenrong Zhang, Jianshu Zhang +2
Table of contents (ToC) extraction aims to extract headings of different levels in documents to better understand the outline of the contents, which can be widely used for document…
Vision-Language Adaptive Mutual Decoder for OOV-STR
Jinshui Hu, Chenyu Liu, Qiandong Yan +4
Recent works have shown huge success of deep learning models for common in vocabulary (IV) scene text recognition. However, in real-world scenarios, out-of-vocabulary (OOV) words a…
SGBANet: Semantic GAN and Balanced Attention Network for Arbitrarily Oriented Scene Text Recognition
Dajian Zhong, Shujing Lyu, Palaiahnakote Shivakumara +4
Scene text recognition is a challenging task due to the complex backgrounds and diverse variations of text instances. In this paper, we propose a novel Semantic GAN and Balanced At…