2024
Sim-to-Real of Soft Robots with Learned Residual Physics
Junpeng Gao, Mike Y. Michelis, Andrew Spielberg, Robert K. Katzschmann
IEEE Robotics and Automation Letters (RA-L) 2024 Best Paper Award 2024
Our paper on sim-to-real transfer of soft robots using learned residual physics was accepted by RA-L and received the Best Paper Award as one of only five papers for the year 2024 from among more than 1,500 papers published in RA-L during 2024.
Sim-to-Real of Soft Robots with Learned Residual Physics
Junpeng Gao, Mike Y. Michelis, Andrew Spielberg, Robert K. Katzschmann
IEEE Robotics and Automation Letters (RA-L) 2024 Best Paper Award 2024
Our paper on sim-to-real transfer of soft robots using learned residual physics was accepted by RA-L and received the Best Paper Award as one of only five papers for the year 2024 from among more than 1,500 papers published in RA-L during 2024.
2022
M2N: Mesh Movement Networks for PDE Solvers
Wenbin Song, Mingrui Zhang, Joseph G Wallwork, Junpeng Gao, Zheng Tian, Fanglei Sun, Matthew Piggott, Junqing Chen, Zuoqiang Shi, Xiang Chen
Advances in Neural Information Processing Systems (NeurIPS) 2022
We present Mesh Movement Networks (M2N), a novel approach for learning mesh movement in PDE solvers. Our method combines neural networks with traditional mesh adaptation techniques to improve computational efficiency and accuracy.
M2N: Mesh Movement Networks for PDE Solvers
Wenbin Song, Mingrui Zhang, Joseph G Wallwork, Junpeng Gao, Zheng Tian, Fanglei Sun, Matthew Piggott, Junqing Chen, Zuoqiang Shi, Xiang Chen
Advances in Neural Information Processing Systems (NeurIPS) 2022
We present Mesh Movement Networks (M2N), a novel approach for learning mesh movement in PDE solvers. Our method combines neural networks with traditional mesh adaptation techniques to improve computational efficiency and accuracy.