no code implementations • 3 Feb 2022 • Mohi Khansari, Daniel Ho, Yuqing Du, Armando Fuentes, Matthew Bennice, Nicolas Sievers, Sean Kirmani, Yunfei Bai, Eric Jang
To the best of our knowledge, this is the first work to tackle latched door opening from a purely end-to-end learning approach, where the task of navigation and manipulation are jointly modeled by a single neural network.
no code implementations • 29 Sep 2021 • Michelle Guo, Wenhao Yu, Daniel Ho, Jiajun Wu, Yunfei Bai, Karen Liu, Wenlong Lu
In addition, we perform two studies showing that UC-DiffOSI operates well in environments with changing or unknown dynamics.
no code implementations • 23 Nov 2020 • Zhuo Xu, Wenhao Yu, Alexander Herzog, Wenlong Lu, Chuyuan Fu, Masayoshi Tomizuka, Yunfei Bai, C. Karen Liu, Daniel Ho
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics.
no code implementations • ICLR 2020 • Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn
Imitation learning allows agents to learn complex behaviors from demonstrations.
1 code implementation • 28 Sep 2019 • Wenhao Yu, Jie Tan, Yunfei Bai, Erwin Coumans, Sehoon Ha
The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases.
no code implementations • 21 Jun 2019 • Xinchen Yan, Mohi Khansari, Jasmine Hsu, Yuanzheng Gong, Yunfei Bai, Sören Pirk, Honglak Lee
Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data.
no code implementations • 10 Jun 2019 • Sören Pirk, Mohi Khansari, Yunfei Bai, Corey Lynch, Pierre Sermanet
We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics.
no code implementations • 7 Jun 2019 • Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn
Imitation learning allows agents to learn complex behaviors from demonstrations.
no code implementations • 27 Apr 2018 • Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, Vincent Vanhoucke
The control policies are learned in a physics simulator and then deployed on real robots.
no code implementations • 17 Oct 2017 • Kuan Fang, Yunfei Bai, Stefan Hinterstoisser, Silvio Savarese, Mrinal Kalakrishnan
Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels.
1 code implementation • 22 Sep 2017 • Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke
We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.
1 code implementation • 24 Aug 2017 • Xinchen Yan, Jasmine Hsu, Mohi Khansari, Yunfei Bai, Arkanath Pathak, Abhinav Gupta, James Davidson, Honglak Lee
Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations.