I am a PhD candidate in computer science (robotics & AI) at the Interactive and Collaborative Autonomous Robotic Systems (ICAROS) Lab advised by Prof. Stefanos Nikolaidis at the University of Southern California (USC). Before starting my PhD, I received my Master's degree from USC, where I worked as a research assistant at the Robotic Embeded Systems Laboratory (RESL) and was advised by Prof. Gaurav Sukhatme and Prof. Stefan Schaal. I got my bachelor's degree from Zhejiang University (ZJU), where I started my study on robotics.
My research goal is to enable robots to intelligently perform manipulation tasks in unstructured human environments.
To achieve my research goal, I am interested in developing fundamental robot learning and planning algorithms for robot manipulation tasks. More specifically, I focus on developing algorithms to enable robots to perform complex, long-horizon manipulation tasks fast and collaboratively.
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Hejia Zhang, Shao-Hung Chan, Jie Zhong, Jiaoyang Li, Peter Kolapo, Sven Koenig, Zach Agioutantis, Steven Schafrik, Stefanos Nikolaidis Autonomous Robots (AURO), 2023 [Coming soon!] This work is an extended version of our previous work on multi-robot geometric task-and-motion planning (MR-GTAMP). We conduct an application study on the roof-bolting task, which is an essential operation within the underground mining cycle. We conduct additional scalability evaluation experiments. We substantially expand the description of the previous MR-GTAMP framework. |
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Hejia Zhang, Shao-Hung Chan, Jie Zhong, Jiaoyang Li, Sven Koenig, Stefanos Nikolaidis IEEE International Conference on Automation Science and Engineering (CASE), 2022 Also at SCR 2022. [PDF] [Slides] We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The key insight is that pre-computed manipulation capabilities of individual robots can be used to guide multi-robot planning. |
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Hejia Zhang, Jie Zhong, Stefanos Nikolaidis Technical Report, 2020 Also at Emergent Behaviors in Human-Robot Systems @ RSS 2020. Featured as Paper of the Month by Kinova Robotics. [PDF] [Slides] [Talk] Previous work has shown that the space of human manipulation actions has a linguistic, hierarchical structure that relates actions to manipulated objects and tools. Building upon this theory of language for action, we propose a system for understanding and executing demonstrated action sequences from full-length, real-world cooking videos on the web. |
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Hejia Zhang, Po-Jen Lai, Sayan Paul, Suraj Kothawade and Stefanos Nikolaidis International Symposium on Robotics Research (ISRR), 2019 [BibTeX] [PDF] We present a system for knowledge acquisition of collaborative manipulation action plans that outputs commands to the robot in the form of visual sentence. |
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Shivin Dass*, Karl Pertsch*, Hejia Zhang, Youngwoon Lee, Joseph J. Lim, Stefanos Nikolaidis Robotics: Science and Systems (RSS), 2023 Also at Pre-training for Robot Learning @ CoRL 2022. [Code] [PDF] [Project] We enable scalable robot data collection by assisting human teleoperators with a learned policy. Our approach estimates its uncertainty over future actions to determine when to request user input. In real world user studies we demonstrate that our system enables more efficient teleoperation with reduced mental load and up to four robots in parallel. |
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Ryan Julian, Eric Heiden, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav S. Sukhatme, Karol Hausman International Journal of Robotics Research (IJRR), 2020 Also at Deep RL Workshop @ NeurIPS 2018. [PDF] This is an extended verion of our previous work on sim-to-real transfer. We show an algorithm which allows our sim-to-real method to perform long-horizon tasks never seen in simulation, by intelligently sequencing short-horizon latent skills. |
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Ryan Julian*, Eric Heiden*, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav S. Sukhatme, Karol Hausman International Symposium on Experimental Robotics (ISER), 2018 [BibTeX] [PDF] We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. |
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Varun Bhatt, Heramb Nemlekar, Matthew C. Fontaine, Bryon Tjanaka, Hejia Zhang, Ya-Chuan Hsu, Stefanos Nikolaidis Conference on Robot Learning (CoRL), 2023 (Oral Presentation; 6.6% acceptance rate) Also at EGG @ RSS 2023. [PDF] We propose augmenting scenario generation systems with surrogate models that predict both human and robot behaviors. In the shared control teleoperation domain and a more complex shared workspace collaboration task, we show that surrogate assisted scenario generation efficiently synthesizes diverse datasets of challenging scenarios. |
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Hejia Zhang*, Matthew Fontaine*, Amy Hoover, Julian Togelius, Bistra Dilkina, Stefanos Nikolaidis AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2020 (Oral Presentation; 25% acceptance rate) [BibTeX] [Code] [PDF] We propose a “generate-then-repair” framework for automatic generation of playable levels adhering to specific styles. |