I am a third year PhD student, co-advised by Professor Mridul Aanjaneya (primary) and Professor Kostas Bekris (secondary). My research is on developing differentiable simulation methods for robotics by combining physics first-principles and machine learning models.
Publications:
2024 |
Chen, N; Wang, K; Johnson, W; Kramer-Bottiglio, R; Bekris, K; Aanjaneya, M Learning Differentiable Tensegrity Dynamics Using Graph Neural Networks Inproceedings Conference on Robot Learning (CoRL), Munich, Germany, 2024. @inproceedings{Chen:2024aa, title = {Learning Differentiable Tensegrity Dynamics Using Graph Neural Networks}, author = {N Chen and K Wang and W Johnson and R Kramer-Bottiglio and K Bekris and M Aanjaneya}, url = {https://openreview.net/pdf?id=5Awumz1VKU}, year = {2024}, date = {2024-11-01}, booktitle = {Conference on Robot Learning (CoRL)}, address = {Munich, Germany}, abstract = {Tensegrity robots are composed of rigid struts and flexible cables and constitute an emerging class of hybrid rigid-soft robotic systems. They are promising systems for a wide-array of applications, ranging from locomotion to assembly. They are difficult to control and model accurately, however, due to their high number of degrees of freedom and compliance. To address this issue, prior works have introduced a differentiable physics engine designed for tensegrity robots based on first-principles. In contrast, this work proposes the use of graph neural networks to model contact dynamics over a graph representation of tensegrity robots, which leverages the natural graph-like cable connectivity between the rod end caps. This learned simulator can accurately model 3-bar and 6-bar tensegrity robot dynamics in simulation-to-simulation experiments where MuJoCo is used as the ground truth. It can also achieve higher accuracy than the previous differentiable engine for a real 3-bar tensegrity robot, where the robot state is only being partially observable. When compared against direct applications of recent graph neural network simulators, the proposed approach is computationally more efficient both for training and inference, while achieving higher accuracy.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Tensegrity robots are composed of rigid struts and flexible cables and constitute an emerging class of hybrid rigid-soft robotic systems. They are promising systems for a wide-array of applications, ranging from locomotion to assembly. They are difficult to control and model accurately, however, due to their high number of degrees of freedom and compliance. To address this issue, prior works have introduced a differentiable physics engine designed for tensegrity robots based on first-principles. In contrast, this work proposes the use of graph neural networks to model contact dynamics over a graph representation of tensegrity robots, which leverages the natural graph-like cable connectivity between the rod end caps. This learned simulator can accurately model 3-bar and 6-bar tensegrity robot dynamics in simulation-to-simulation experiments where MuJoCo is used as the ground truth. It can also achieve higher accuracy than the previous differentiable engine for a real 3-bar tensegrity robot, where the robot state is only being partially observable. When compared against direct applications of recent graph neural network simulators, the proposed approach is computationally more efficient both for training and inference, while achieving higher accuracy. |