6 papers · 1 filter
MemoryWAM: Efficient World Action Modeling with Persistent Memory
Sizhe Yang, Juncheng Mu, Tianming Wei +8
Robust robotic manipulation in the real world requires not only an understanding of the current observation, but also memory and dynamics modeling. World action models (WAMs) posse…
Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation
Feiyu Jia, Xiaojie Niu, Sizhe Yang +5
Teleoperation is a key approach for collecting high-quality, physically consistent demonstrations for robotic manipulation. However, teleoperation for dexterous manipulation remain…
One-Policy-Fits-All: Geometry-Aware Action Latents for Cross-Embodiment Manipulation
Juncheng Mu, Sizhe Yang, Hojin Bae +5
Cross-embodiment manipulation is crucial for enhancing the scalability of robot manipulation and reducing the high cost of data collection. However, the significant differences bet…
UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data
Sizhe Yang, Yiman Xie, Zhixuan Liang +4
Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the…
DexImit: Learning Bimanual Dexterous Manipulation from Monocular Human Videos
Juncheng Mu, Sizhe Yang, Yiming Bao +6
Data scarcity fundamentally limits the generalization of bimanual dexterous manipulation, as real-world data collection for dexterous hands is expensive and labor-intensive. Human…
Robo3R: Enhancing Robotic Manipulation with Accurate Feed-Forward 3D Reconstruction
Sizhe Yang, Linning Xu, Hao Li +4
3D spatial perception is fundamental to generalizable robotic manipulation, yet obtaining reliable, high-quality 3D geometry remains challenging. Depth sensors suffer from noise an…