Zoey Chen

Selected Publications

1. 

   URDFormer: Constructing Interactive Realistic Scenes from Real Images via Simulation and Generative Modeling

   Zoey Chen, Aaron Walsman, Marius Memmel, Kaichun Mo, Alex Fang, Karthikeya Vemuri, Alan Wu, Dieter Fox, and Abhishek Gupta

   In Robotics: Science and Systems (RSS), 2024

   Oral Presentation at CoRL TGR workshop

   Abstract Paper Code Website

   Constructing accurate and targeted simulation scenes that are both visually and physically realistic is a significant practical interest in domains ranging from robotics to computer vision. However, this process is typically done largely by hand - a graphic designer and a simulation engineer work together with predefined assets to construct rich scenes with realistic dynamic and kinematic properties. While this may scale to small numbers of scenes, to achieve the generalization properties that are requisite of data-driven machine learning algorithms, we require a pipeline that is able to synthesize large numbers of realistic scenes, c omplete with “natural” kinematic and dynamic structure. To do so, we develop models for inferring structure and generating simulation scenes from natural images, allowing for scalable scene generation from web-scale datasets. To train these image-to-simulation models, we show how effective generative models can be used in generating training data, the network can be inverted to map from realistic images back to complete scene models. We show how this paradigm allows us to build large datasets of scenes with semantic and physical realism, enabling a variety of downstream applications in robotics and computer vision.

2. 

   GenAug: Retargeting Behaviors to Unseen Situations via Generative Augmentation

   Zoey Chen, Sho Kiami, Abhishek Gupta, and Vikash Kumar

   In Robotics: Science and Systems (RSS), 2023

   Best System Paper Finalist

   Abstract Paper Code Poster Slides Website

   Robot learning methods have the potential for widespread generalization across tasks, environments, and objects. However, these methods require large diverse datasets that are expensive to collect in real-world robotics settings. For robot learning to generalize, we must be able to leverage sources of data or priors beyond the robot’s own experience. In this work, we posit that image-text generative models, which are pre-trained on large corpora of web-scraped data, can serve as such a data source. We show that despite these generative models being trained on largely non-robotics data, they can serve as effective ways to impart priors into the process of robot learning in a way that enables widespread generalization. In particular, we show how pre-trained generative models can serve as effective tools for semantically meaningful data augmentation. By leveraging these pre-trained models for generating appropriate “semantic” data augmentations, we propose a system GenAug that is able to significantly improve policy generalization. We apply GenAug to tabletop manipulation tasks, showing the ability to re-target behavior to novel scenarios, while only requiring marginal amounts of real-world data.

3. 

   Learning Robust Real-world Dexterous Grasping Policies via Implicit Shape Augmentation

   Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao, Wei Yang, Arsalan Mousavian, Abhishek Gupta, and Dieter Fox

   In Conference on Robot Learning (CoRL), 2022

   Abstract Paper Website

   Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping. However, learning robust real world grasping policies for arbitrary objects has proven challenging due to the difficulty of generating high quality training data. In this work, we propose a learning system (ISAGrasp) for leveraging a small number of human demonstrations to bootstrap the generation of a much larger dataset containing successful grasps on a variety of novel objects. Our key insight is to use a correspondenceaware implicit generative model to deform object meshes and demonstrated human grasps in order to generate a diverse dataset of novel objects and successful grasps for supervised learning, while maintaining semantic realism. We use this dataset to train a robust grasping policy in simulation which can be deployed in the real world.