Xiao Ma (马骁)
I am a Lead Researcher at Dyson Robot Learning Lab. I obtained my PhD from 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. Previously, I have spent wonderful time at Sea AI Lab, hosted by Prof. Shuicheng Yan and Dr. Min Lin, and at SenseTime Research, hosted by Dr. Shuai Yi.
I'm broadly interested in reinforcement learning, representation learning, information theory, and their applications to robot learning in unstructured environments.
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News
- [Feb. 2023] 1 paper accepted to CVPR 2023.
- [Feb. 2023] I joined Dyson Robot Learning Lab as a Lead Researcher.
- [Jan. 2023] 3 papers accepted to ICLR 2023 (1 oral 2 posters)!
- [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!
- [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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Imitation Learning via Differentiable Physics
Siwei Chen,
Xiao Ma,
Zhongwen Xu
Computer Vision and Pattern Recognition (CVPR), 2023  
project page (coming soon)
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We present Imitation Learning via Differentiable Physics (ILD), which casts the imitation learning as a state-matching task through differentiable physics-based Chamfer distance loss. ILD significantly improves the sample efficiency and generalization of imitation learning algorithms with only one expert demonstration.
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DaxBench: Benchmarking Deformable Object Manipulation with Differentiable Physics
Siwei Chen*,
Cunjun Yu*,
Yiqing Xu*,
Linfeng Li,
Xiao Ma,
Zhongwen Xu,
David Hsu
(*equal contributions)
International Conference on Learning Representations (ICLR), 2023   (Oral)
project page (coming soon)
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bibtex
We present DaXBench, a comprehensive benchmark for deformable object manipulation, including planning, imitation learning, and reinforcement learning, based on a scalable and differentiable physics simulator coded in JAX.
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DiffMimic: Efficient Motion Mimicking with Differentiable Physics
Jiawei Ren*,
Cunjun Yu*,
Siwei Chen,
Xiao Ma,
Liang Pan,
Ziwei Liu,
(*equal contributions)
International Conference on Learning Representations (ICLR), 2023  
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bibtex
DiffMimic scales motion imitation for simulated characters with differentiable physics. Training controllers on large-scale motion database is more accessible with DiffMimic.
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RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning
Wei Qiu,
Xiao Ma,
Bo An,
Svetlana Obraztsova,
Shuicheng Yan,
Siwei Chen*,
Zhongwen Xu,
International Conference on Learning Representations (ICLR), 2023
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We present Ranked Policy Memory (RPM) which simulates unseen agents by ranking history agents in multi-agent RL to encourage better generalization during evaluation.
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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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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,
Yunfan Lu,
David Hsu,
Robotics: Science and Systems (RSS), 2021  
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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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