6 papers
AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning
Zhiyue Zhao, Jingyi Wu, Hairuo Liu +5
Dexterous manipulation is a fundamental capability for embodied intelligence, but scaling it remains difficult because robot demonstrations are expensive to collect and action spac…
TaCo: A Benchmark for Lossless and Lossy Codecs of Heterogeneous Tactile Data
Zhengxue Cheng, Yan Zhao, Keyu Wang +2
Tactile sensing is crucial for embodied intelligence, providing fine-grained perception and control in complex environments. However, efficient tactile data compression, which is e…
RoboPaint: From Human Demonstration to Any Robot and Any View
Jiacheng Fan, Zhiyue Zhao, Yiqian Zhang +4
Acquiring large-scale, high-fidelity robot demonstration data remains a critical bottleneck for scaling Vision-Language-Action (VLA) models in dexterous manipulation. We propose a…
D3Grasp: Diverse and Deformable Dexterous Grasping for General Objects
Keyu Wang, Bingcong Lu, Zhengxue Cheng +2
Achieving diverse and stable dexterous grasping for general and deformable objects remains a fundamental challenge in robotics, due to high-dimensional action spaces and uncertaint…
OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing
Zhengxue Cheng, Yiqian Zhang, Anni Tang +5
Recent vision-language-action (VLA) models build upon vision-language foundations, and have achieved promising results and exhibit the possibility of task generalization in robot m…
TacCompress: A Benchmark for Multi-Point Tactile Data Compression in Dexterous Hand
Yan Zhao, Yang Li, Zhengxue Cheng +2
Though robotic dexterous manipulation has progressed substantially recently, challenges like in-hand occlusion still necessitate fine-grained tactile perception, leading to the int…