1 code implementation • 26 Jan 2024 • Yue Yang, Atith N Gandhi, Greg Turk
These annotations provide additional structure that coax the generative model to produce higher quality hand images.
no code implementations • 24 Jun 2023 • J. Taery Kim, Wenhao Yu, Yash Kothari, Jie Tan, Greg Turk, Sehoon Ha
To build a successful guide robot, our paper explores three key topics: (1) formalizing the navigation mechanism of a guide dog and a human, (2) developing a data-driven model of their interaction, and (3) improving user safety.
no code implementations • 14 Mar 2023 • Yunbo Zhang, Alexander Clegg, Sehoon Ha, Greg Turk, Yuting Ye
In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects.
1 code implementation • 7 Feb 2023 • Yanzhe Zhang, Lu Jiang, Greg Turk, Diyi Yang
Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools.
no code implementations • 1 Nov 2021 • Fabio Muratore, Fabio Ramos, Greg Turk, Wenhao Yu, Michael Gienger, Jan Peters
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data.
1 code implementation • 20 May 2021 • Henry M. Clever, Patrick Grady, Greg Turk, Charles C. Kemp
We present a method that infers contact pressure between a human body and a mattress from a depth image.
no code implementations • 3 Mar 2021 • Yunbo Zhang, Wenhao Yu, C. Karen Liu, Charles C. Kemp, Greg Turk
We produce a final animation by using inverse kinematics to guide a character's arm and hand to match the motion of the manipulation tool such as a knife or a frying pan.
no code implementations • 11 Dec 2020 • Wenhao Yu, C. Karen Liu, Greg Turk
When used with a set of thresholds, the safety estimator becomes a classifier for switching between the protective policy and the task policy.
1 code implementation • CVPR 2020 • Henry M. Clever, Zackory Erickson, Ariel Kapusta, Greg Turk, C. Karen Liu, Charles C. Kemp
We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes.
3D human pose and shape estimation 3D Human Shape Estimation +1
no code implementations • 14 Sep 2019 • Alexander Clegg, Zackory Erickson, Patrick Grady, Greg Turk, Charles C. Kemp, C. Karen Liu
We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing.
no code implementations • 13 May 2019 • Yunbo Zhang, Wenhao Yu, Greg Turk
Our method does this by creating a second reward function that recognizes previously seen state sequences and rewards those by novelty, which is measured using autoencoders that have been trained on state sequences from previously discovered policies.
no code implementations • 4 Mar 2019 • Wenhao Yu, Visak CV Kumar, Greg Turk, C. Karen Liu
We present a new approach for transfer of dynamic robot control policies such as biped locomotion from simulation to real hardware.
1 code implementation • ICLR 2019 • Wenhao Yu, C. Karen Liu, Greg Turk
Transfer learning using domain randomization is a promising approach, but it usually assumes that the target environment is close to the distribution of the training environments, thus relying heavily on accurate system identification.
2 code implementations • 24 Jan 2018 • Wenhao Yu, Greg Turk, C. Karen Liu
Indeed, a standard benchmark for DRL is to automatically create a running controller for a biped character from a simple reward function.
no code implementations • 27 Sep 2017 • Zackory Erickson, Henry M. Clever, Greg Turk, C. Karen Liu, Charles C. Kemp
The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body.
no code implementations • 23 Sep 2017 • Wenhao Yu, C. Karen Liu, Greg Turk
Then, during the specialization training stage we selectively split the weights of the policy based on a per-weight metric that measures the disagreement among the multiple tasks.
1 code implementation • 8 Feb 2017 • Wenhao Yu, Jie Tan, C. Karen Liu, Greg Turk
Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment.