6 citations · 12 across the 15 of their papers we have counts for
16 papers
ForceDelta-VLA: Distilling Force-Conditioned Action Corrections for Contact-Rich Manipulation
Ju Dong, Yu Fu, Jian Chen +9
Force-aware Vision-Language-Action (VLA) policies improve contact-rich manipulation, but typically combine task-level motion and contact-dependent adjustment in a single action pre…
From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
Ju Dong, Liding Zhang, Lei Zhang +7
Generative policies based on diffusion and flow matching achieve strong performance in robotic manipulation by modeling multi-modal human demonstrations. However, their reliance on…
ResponsibleRobotBench: Benchmarking Responsible Robot Manipulation using Multi-modal Large Language Models
Lei Zhang, Ju Dong, Kaixin Bai +4
Recent advances in large multimodal models have enabled new opportunities in embodied AI, particularly in robotic manipulation. These models have shown strong potential in generali…
OmniDexVLG: Learning Dexterous Grasp Generation from Vision Language Model-Guided Grasp Semantics, Taxonomy and Functional Affordance
Lei Zhang, Diwen Zheng, Kaixin Bai +5
Dexterous grasp generation aims to produce grasp poses that align with task requirements and human interpretable grasp semantics. However, achieving semantically controllable dexte…
FUNCanon: Learning Pose-Aware Action Primitives via Functional Object Canonicalization for Generalizable Robotic Manipulation
Hongli Xu, Lei Zhang, Xiaoyue Hu +7
General-purpose robotic skills from end-to-end demonstrations often leads to task-specific policies that fail to generalize beyond the training distribution. Therefore, we introduc…
M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation
Ju Dong, Lei Zhang, Liding Zhang +8
Mobile manipulation requires the coordinated control of a mobile base and a robotic arm while simultaneously perceiving both global scene context and fine-grained object details. E…
DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation
Lei Zhang, Soumya Mondal, Zhenshan Bing +5
Dexterous robotic manipulation remains a longstanding challenge in robotics due to the high dimensionality of control spaces and the semantic complexity of object interaction. In t…
A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI
Lik Hang Kenny Wong, Xueyang Kang, Kaixin Bai +1
Navigation and manipulation are core capabilities in Embodied AI, but training agents to perform them directly in the real world is costly, time-consuming, and unsafe. Therefore, s…
Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy
Minheng Ni, Lei Zhang, Zihan Chen +4
Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks, such as poisonings, fires, and even explosions. In this paper, we present respon…
ClearDepth: Enhanced Stereo Perception of Transparent Objects for Robotic Manipulation
Kaixin Bai, Huajian Zeng, Lei Zhang +4
Transparent object depth perception poses a challenge in everyday life and logistics, primarily due to the inability of standard 3D sensors to accurately capture depth on transpare…
Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation
Kaixin Bai, Lei Zhang, Zhaopeng Chen +2
Despite the substantial progress in deep learning, its adoption in industrial robotics projects remains limited, primarily due to challenges in data acquisition and labeling. Previ…
ContactDexNet: Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping
Lei Zhang, Kaixin Bai, Guowen Huang +4
The deep learning models has significantly advanced dexterous manipulation techniques for multi-fingered hand grasping. However, the contact information-guided grasping in cluttere…
ToolEENet: Tool Affordance 6D Pose Estimation
Yunlong Wang, Lei Zhang, Yuyang Tu +4
The exploration of robotic dexterous hands utilizing tools has recently attracted considerable attention. A significant challenge in this field is the precise awareness of a tool's…
A Collision-Aware Cable Grasping Method in Cluttered Environment
Lei Zhang, Kaixin Bai, Qiang Li +2
We introduce a Cable Grasping-Convolutional Neural Network designed to facilitate robust cable grasping in cluttered environments. Utilizing physics simulations, we generate an ext…
A Closed-Loop Multi-perspective Visual Servoing Approach with Reinforcement Learning
Lei Zhang, Jiacheng Pei, Kaixin Bai +2
Traditional visual servoing methods suffer from serving between scenes from multiple perspectives, which humans can complete with visual signals alone. In this paper, we investigat…
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning
Lei Zhang, Kaixin Bai, Zhaopeng Chen +2
Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. Most of the current methods for model-free grasping are disadvant…