Learning to Navigate Cloth using Haptics. Alexander Clegg, Wenhao Yu, Zackory Erickson, Jie Tan, Karen Liu, Greg Turk. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2017.

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information.

[Project    Paper]

Preparing for the Unknown: Learning a Universal Policy with Online System Identification. Wenhao Yu, Jie Tan, Karen Liu, Greg Turk. Robotics: Science and Systems (RSS). 2017.

We present a new method of learning control policies that successfully operate under unknown dynamic models. We create such policies by leveraging a large number of training examples that are generated using a physical simulator.

[Paper]

Haptic Simulation for Robot-Assisted Dressing. Wenhao Yu, Ariel Kapusta, Jie Tan, Charles C. Kemp, Greg Turk, Karen Liu. IEEE International Conference on Robotics and Automation (ICRA), 2017.

We focus on a representative dressing task of pulling the sleeve of a hospital gown onto a person’s arm. We present a system that learns a haptic classifier for the outcome of the task given few (2-3) real-world trials with one person.

[Paper]

Large-Scale Evolution of Image Classifiers. Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, Alexey Kurakin. International Conference on Machine Learning (ICML), 2017.

Designing architectures for neural networks can be challenging. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically.

[Paper]

Simulation-Based Design of Dynamic Controllers for Humanoid Balancing. Jie Tan, Zhaoming Xie, Byron Boots, Karen Liu. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2016.

we propose a complete system that automatically designs a humanoid robotic controller that succeeds on tasks in the real world with a very small number of real world experiments.

[Project    Paper    Video]

Animating Human Dressing. Alexander Clegg, Jie Tan, Greg Turk, Karen Liu. ACM Transactions on Graphics 33(4), SIGGRAPH 2014.

We present a technique to synthesize human dressing by controlling a human character to put on an article of simulated clothing.

[Project    Paper    Video]

Learning Bicycle Stunts. Jie Tan, Yuting Gu, Karen Liu, Greg Turk. ACM Transactions on Graphics 33(4), SIGGRAPH 2014.

We apply reinforcement learning to find the optimal policies that allows a human character to perform bicycle stunts in a physically simulated environment.

[Project    Paper    Supplementary Doc    BibTeX    Video]

Soft Body Locomotion. Jie Tan, Greg Turk, Karen Liu. ACM Transactions on Graphics 31(4), SIGGRAPH 2012.

We present a physically-based system to simulate the locomotion of soft body characters without skeletons. To control the locomotion, we formulate and solve a quadratic program with complementary conditions (QPCC) to plan the muscle contraction and the contact forces simultaneously.

[Project    Paper    Supplementary Doc    BibTeX    Video]

Articulated Swimming Creatures. Jie Tan, Yuting Gu, Greg Turk, Karen Liu. ACM Transactions on Graphics 30(4), SIGGRAPH 2011.

We present a general approach to creating realistic swimming behavior for a given articulated creature body. We simulate the simultaneous two-way coupling between the fluid and the creature and apply numerical optimization to find the most efficient swimming gait for the animal.

[Project    Paper    BibTeX    Video]

Stable Proportional-Derivative Controllers. Jie Tan, Karen Liu, Greg Turk. IEEE Computer Graphics and Applications, 31(4), 2011.

We reformulate the traditional PD controller by taking into account the character's positions and velocities in the next time step, which allows arbitrarily high gains, even at large time steps.

[Project    Paper    BibTeX    Video]

Physically-based Fluid Animation: A Survey. Jie Tan, Xubo Yang. Science In China Series F: Information Science, 52(5), 2009.

We give an comprehensive survey on physically-based fluid animation research.

[Paper    BibTeX]

Fluid Animation with Multilayer Grids. Jie Tan, Xubo Yang, Xin Zhao, Zhanxin Yang. ACM SIGGRAPH/Eurographics Symposium of Computer Animation Poster, 2008.

We propose a multi-layer grid structure to numerically solve the Navier-Stokes equations, which enables combining advantages of various discretizations, catching the multi-scale behavior and optimizing the computational resources.

[Paper    BibTeX    Video]