10 citations · 29 across the 14 of their papers we have counts for
14 papers
Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation
Minjie Zhu, Yichen Zhu, Jinming Li +8
Diffusion Policy is a powerful technique tool for learning end-to-end visuomotor robot control. It is expected that Diffusion Policy possesses scalability, a key attribute for deep…
TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation
Junjie Wen, Yichen Zhu, Jinming Li +9
Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA…
Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models
Minjie Zhu, Yichen Zhu, Xin Liu +7
Multimodal Large Language Models (MLLMs) have showcased impressive skills in tasks related to visual understanding and reasoning. Yet, their widespread application faces obstacles…
Language-Conditioned Robotic Manipulation with Fast and Slow Thinking
Minjie Zhu, Yichen Zhu, Jinming Li +8
The language-conditioned robotic manipulation aims to transfer natural language instructions into executable actions, from simple pick-and-place to tasks requiring intent recogniti…
Object-Centric Instruction Augmentation for Robotic Manipulation
Junjie Wen, Yichen Zhu, Minjie Zhu +8
Humans interpret scenes by recognizing both the identities and positions of objects in their observations. For a robot to perform tasks such as \enquote{pick and place}, understand…
Visual Robotic Manipulation with Depth-Aware Pretraining
Wanying Wang, Jinming Li, Yichen Zhu +7
Recent work on visual representation learning has shown to be efficient for robotic manipulation tasks. However, most existing works pretrained the visual backbone solely on 2D ima…
Exploring Gradient Explosion in Generative Adversarial Imitation Learning: A Probabilistic Perspective
Wanying Wang, Yichen Zhu, Yirui Zhou +5
Generative Adversarial Imitation Learning (GAIL) stands as a cornerstone approach in imitation learning. This paper investigates the gradient explosion in two types of GAIL: GAIL w…
CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
Mingze Wei, Yaomin Huang, Zhiyuan Xu +7
In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly red…
CP: Channel Pruning Plug-in for Point-based Networks
Yaomin Huang, Ning Liu, Zhengping Che +7
Channel pruning can effectively reduce both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success ha…
Label-Guided Auxiliary Training Improves 3D Object Detector
Yaomin Huang, Xinmei Liu, Yichen Zhu +7
Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently. In this paper, we propose a Label-Guided auxiliary trai…
Hybrid Atlas Building with Deep Registration Priors
Nian Wu, Jian Wang, Miaomiao Zhang +3
Registration-based atlas building often poses computational challenges in high-dimensional image spaces. In this paper, we introduce a novel hybrid atlas building algorithm that fa…
Gleason Score Prediction using Deep Learning in Tissue Microarray Image
Yi-hong Zhang, Jing Zhang, Yang Song +2
Prostate cancer (PCa) is one of the most common cancers in men around the world. The most accurate method to evaluate lesion levels of PCa is microscopic inspection of stained biop…
Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix
Chaomin Shen, Yaxin Peng, Guixu Zhang +1
We propose a scheme for defending against adversarial attacks by suppressing the largest eigenvalue of the Fisher information matrix (FIM). Our starting point is one explanation on…
The Adversarial Attack and Detection under the Fisher Information Metric
Chenxiao Zhao, P. Thomas Fletcher, Mixue Yu +3
Many deep learning models are vulnerable to the adversarial attack, i.e., imperceptible but intentionally-designed perturbations to the input can cause incorrect output of the netw…