Mo, Kaichun

SAPIEN: A SimulAted Part-Based Interactive ENvironment

Xiang, F., Qin, Y., Mo, K., Xia, Y., Zhu, H., Liu, F., Liu, M., Jiang, H., Yuan, Y., Wang, H., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 406

Where2Act: From Pixels to Actions for Articulated 3D Objects

Mo, K., Guibas, L., Mukadam, M., Gupta, A., & Tulsiani, S. (2021) · arXiv

Robot Manipulation and LearningAdvanced Neural Network Applications

Cited by 132

AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions

Wang, Y., Wu, R., Mo, K., Ke, J., Fan, Q., Guibas, L. J., & Dong, H. (2022) · Lecture Notes in Computer Science

Robot Manipulation and Learning3D Shape Modeling and Analysis

Cited by 40

Cosmos World Foundation Model Platform for Physical AI

NVIDIA, :, Agarwal, N., Ali, A., Bala, M., Balaji, Y., Barker, E., Cai, T., Chattopadhyay, P., Chen, Y., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 13

DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation

Zhao, Y., Wu, R., Chen, Z., Zhang, Y., Fan, Q., Mo, K., & Dong, H. (2022) · arXiv

Robot Manipulation and Learning3D Shape Modeling and Analysis

Cited by 7

World Simulation with Video Foundation Models for Physical AI

NVIDIA, :, Ali, A., Bai, J., Bala, M., Balaji, Y., Blakeman, A., Cai, T., Cao, J., Cao, T., et al. (2025) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 1