42 citations · 56 across the 6 of their papers we have counts for
7 papers
Learning to Manipulate Amorphous Materials
Yunbo Zhang, Wenhao Yu, C. Karen Liu +2
We present a method of training character manipulation of amorphous materials such as those often used in cooking. Common examples of amorphous materials include granular materials…
Protective Policy Transfer
Wenhao Yu, C. Karen Liu, Greg Turk
Being able to transfer existing skills to new situations is a key capability when training robots to operate in unpredictable real-world environments. A successful transfer algorit…
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
Henry M. Clever, Zackory Erickson, Ariel Kapusta +3
People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, yet line-of-sight p…
Learning to Collaborate from Simulation for Robot-Assisted Dressing
Alexander Clegg, Zackory Erickson, Patrick Grady +3
We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to simultaneously train human and r…
Learning Novel Policies For Tasks
Yunbo Zhang, Wenhao Yu, Greg Turk
In this work, we present a reinforcement learning algorithm that can find a variety of policies (novel policies) for a task that is given by a task reward function. Our method does…
Learning to Navigate Cloth using Haptics
Alexander Clegg, Wenhao Yu, Zackory Erickson +3
We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a…
Preparing for the Unknown: Learning a Universal Policy with Online System Identification
Wenhao Yu, Jie Tan, C. Karen Liu +1
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 exampl…