Xiao Ma (马骁)
I am a research scientist at SEA AI Lab. I pursued my PhD at National University of Singapore, advised by Prof. David Hsu. I also worked closely with Prof. Wee Sun Lee on my projects. I received my B.Sc. in Computer Science from Shanghai Jiao Tong University in 2017, where I was advised by Prof. Fan Wu and Prof. Xiaofeng Gao.
My research focuses on uncertainty modelling, reinforcement learning, graph neural networks, and their applications to robotics.
I'm looking for research interns to work on topics including multi-agent RL / offline RL. Please reach out if you are interested :)
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News
- [Apr. 2022] HILMO for reinforcement learning under mixed observability has been accepted to WAFR 2022!
- [Feb. 2022] G-DOOM for deformable object manipulation has been accepted to ICRA 2022!
- [Nov. 2021] I've successfully defensed my PhD thesis!
- [Oct. 2021] SABRA for object relationship detection under imbalanced distributions has been accepted to BMVC 2021.
- [Jul. 2021] I'm joining SEA AI Lab as a research scientist working on reinforcement learning.
- [May 2021] PROMPT for ab-initio object manipulation has been accepted by RSS 2021.
- [Oct. 2020] CVRL for model-based RL under complex observations has been accepted by CoRL 2020.
- [Sept. 2020] BALMS for long-tailed visual recognition has been accepted by NeurIPS 2020.
- [Jul. 2020] STAR for pedestrian trajectory prediction has been accepted by ECCV 2020.
- [Dec. 2019] DPFRL for reinforcement learning under complex and partial observations has been accepted by ICLR 2020.
- [Nov. 2019] PF-RNNs for sequence modeling under uncertainty has been accepted to AAAI 2020.
- [Jun. 2019] DAN was nominated for the best system paper and best student paper of RSS 2019!
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Learning Latent Graph Dynamics for Deformable Object Manipulation
Xiao Ma,
David Hsu,
Wee Sun Lee,
International Conference on Robotics and Automation (ICRA), 2022  
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bibtex
We present G-DOOM for deformable object manipulation. G-DOOM abstract an deformable object as a keypoint-based graph and models the spatio-temporal keypoint interactions with Recurrent Graph Dynamics. G-DOOM achieves SOTA performance on a set of deformable object manipulation tasks.
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Ab Initio Particle-based Object Manipulation
Siwei Chen,
Xiao Ma,
David Hsu,
Robotics: Science and Systems (RSS), 2021  
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code
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bibtex
This paper introduces PROMPT, a framework for particle-based object manipulation. PROMPT performs high-quality online point cloud reconstruction from multi-view images captured by an eye-in-hand camera. It achieves high performance in object grasping, pushing, and placing.
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