2 citations · 2 across the 10 of their papers we have counts for
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MDPO: Multi-constraint Dexterous Policy Optimization with Reinforcement Learning
Ao Zhou, Zongyou Yu, Ke Wu +5
Dexterous manipulation requires high-dimensional control policies to accomplish complex contact-rich tasks while simultaneously satisfying multiple safety requirements, such as joi…
ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation
Zijun Xu, Wendi Chen, Xinkai Wang +16
Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions. Task-agnostic play provides broa…
Human Universal Grasping
Kevin Yuanbo Wu, Tianxing Zhou, Isaac Tu +5
Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality. We argue that the most natural source of robot grasping data is from hum…
Flash-Mono: Feed-Forward Accelerated Gaussian Splatting Monocular SLAM
Zicheng Zhang, Ke Wu, Xiangting Meng +3
Monocular 3D Gaussian Splatting SLAM suffers from critical limitations in time efficiency, geometric accuracy, and multi-view consistency. These issues stem from the time-consuming…
SparseSplat: Towards Applicable Feed-Forward 3D Gaussian Splatting with Pixel-Unaligned Prediction
Zicheng Zhang, Xiangting Meng, Ke Wu +1
Recent progress in feed-forward 3D Gaussian Splatting (3DGS) has notably improved rendering quality. However, the spatially uniform and highly redundant 3DGS map generated by previ…
CMoE: Contrastive Mixture of Experts for Motion Control and Terrain Adaptation of Humanoid Robots
Shihao Ma, Hongjin Chen, Zijun Xu +6
For effective deployment in real-world environments, humanoid robots must autonomously navigate a diverse range of complex terrains with abrupt transitions. While the Vanilla mixtu…