no code implementations • 18 Apr 2024 • Dominik Bauer, Zhenjia Xu, Shuran Song
Manipulation of elastoplastic objects like dough often involves topological changes such as splitting and merging.
1 code implementation • 19 Jul 2023 • Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, Shuran Song
Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior.
no code implementations • 4 Jul 2023 • Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, Anirudha Majumdar
Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions.
no code implementations • 17 May 2023 • Zhou Xian, Theophile Gervet, Zhenjia Xu, Yi-Ling Qiao, Tsun-Hsuan Wang, Yian Wang
This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots.
1 code implementation • 4 Mar 2023 • Zhou Xian, Bo Zhu, Zhenjia Xu, Hsiao-Yu Tung, Antonio Torralba, Katerina Fragkiadaki, Chuang Gan
We identify several challenges for fluid manipulation learning by evaluating a set of reinforcement learning and trajectory optimization methods on our platform.
1 code implementation • 17 Jul 2022 • Zeyi Liu, Zhenjia Xu, Shuran Song
We introduce BusyBoard, a toy-inspired robot learning environment that leverages a diverse set of articulated objects and inter-object functional relations to provide rich visual feedback for robot interactions.
no code implementations • 13 Sep 2021 • Zhenjia Xu, Zhanpeng He, Shuran Song
We introduce the Universal Manipulation Policy Network (UMPNet) -- a single image-based policy network that infers closed-loop action sequences for manipulating arbitrary articulated objects.
1 code implementation • 28 Nov 2020 • Zhenjia Xu, Beichun Qi, Shubham Agrawal, Shuran Song
We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers.
Robotics
2 code implementations • 3 Nov 2020 • Zhenjia Xu, Zhanpeng He, Jiajun Wu, Shuran Song
3D scene representation for robot manipulation should capture three key object properties: permanency -- objects that become occluded over time continue to exist; amodal completeness -- objects have 3D occupancy, even if only partial observations are available; spatiotemporal continuity -- the movement of each object is continuous over space and time.
no code implementations • 10 Jun 2019 • Zhenjia Xu, Jiajun Wu, Andy Zeng, Joshua B. Tenenbaum, Shuran Song
We study the problem of learning physical object representations for robot manipulation.
no code implementations • ICLR 2019 • Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future.
no code implementations • 12 Mar 2019 • Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future.