3 citations · 3 across the 3 of their papers we have counts for
4 papers
Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation
Jinzhou Li, Tianhao Wu, Jiyao Zhang +6
Effectively utilizing multi-sensory data is important for robots to generalize across diverse tasks. However, the heterogeneous nature of these modalities makes fusion challenging.…
Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping
Qianxu Wang, Congyue Deng, Tyler Ga Wei Lum +5
One-shot transfer of dexterous grasps to novel scenes with object and context variations has been a challenging problem. While distilled feature fields from large vision models hav…
AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents
Jieming Cui, Tengyu Liu, Nian Liu +3
Traditional approaches in physics-based motion generation, centered around imitation learning and reward shaping, often struggle to adapt to new scenarios. To tackle this limitatio…
Dexterous Functional Pre-Grasp Manipulation with Diffusion Policy
Tianhao Wu, Yunchong Gan, Mingdong Wu +4
In real-world scenarios, objects often require repositioning and reorientation before they can be grasped, a process known as pre-grasp manipulation. Learning universal dexterous f…