9 papers
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…
ViTaS: Visual Tactile Soft Fusion Contrastive Learning for Visuomotor Learning
Yufeng Tian, Shuiqi Cheng, Tianming Wei +6
Tactile information plays a crucial role in human manipulation tasks and has recently garnered increasing attention in robotic manipulation. However, existing approaches mostly foc…
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…
HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation
Zhecheng Yuan, Tianming Wei, Langzhe Gu +4
Leveraging human motion data to impart robots with versatile manipulation skills has emerged as a promising paradigm in robotic manipulation. Nevertheless, translating multi-source…
HDP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning
Yiyang Lu, Yufeng Tian, Zhecheng Yuan +4
Visuomotor policy learning has witnessed substantial progress in robotic manipulation, with recent approaches predominantly relying on generative models to model the action distrib…
Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation
Yu Qi, Yuanchen Ju, Tianming Wei +3
3D assembly tasks, such as furniture assembly and component fitting, play a crucial role in daily life and represent essential capabilities for future home robots. Existing benchma…