Xiao Ma (马骁)

I am a research scientist at ByteDance Seed. Prior to that, I pursued my PhD at National University of Singapore, advised by Prof. David Hsu. I have worked as research scientist at Dyson Robot Learning Lab, led by Dr. Stephen James, at Sea AI Lab, hosted by Prof. Shuicheng Yan and Dr. Min Lin, and at SenseTime Research, hosted by Dr. Shuai Yi.

My research spans across reinforcement learning, robotics, and multi-modal models. My long-term goal is to develop intelligent embodied agents that perceive, interact with, and adapt to unstructured physical environments in a shared autonomy with humans.

profile photo

photo credits to Rachel Li Rui ♥

News

[Jan. 2025] 1 paper accepted to ICRA 2025.
[Sept. 2024] 1 paper accepted to CoRL 2024.
[Jul. 2024] I joined ByteDance Seed as a research scientist.
[Jun. 2024] 1 paper accepted to RA-L 2024.
[Feb. 2024] 1 paper accepted to CVPR 2024.
[Sept. 2023] 3 papers accepted to NeurIPS 2023.
[Feb. 2023] 1 paper accepted to CVPR 2023.
[Jan. 2023] 3 papers accepted to ICLR 2023 (1 oral 2 posters)!

Selected Publications (Full publication list)

RoboVLMs
Towards Generalist Robot Policies: What Matters in Building Vision-Language-Action Models
Technical Report, 2024
BiGym
BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark
Conference on Robot Learning (CoRL), 2024
Redundancy-aware Action Spaces
Redundancy-aware Action Spaces for Robot Learning
Pietro Mazzaglia*, Nicholas Backshall*, Xiao Ma, Stephen James (*equal contributions)
IEEE Robotics and Automation Letters (RA-L), 2024
HDP
Hierarchical Diffusion Policy for Multi-Task Robotic Manipulation
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024
InsActor
InsActor: Instruction-driven Physics-based Characters
Conference on Neural Information Processing Systems (NeurIPS), 2023
Efficient Diffusion Policies
Efficient Diffusion Policies for Offline Reinforcement Learning
Bingyi Kang*, Xiao Ma*, Chao Du, Tianyu Pang, Shuicheng Yan (*equal contributions)
Conference on Neural Information Processing Systems (NeurIPS), 2023
Mutual Information Regularized Offline Reinforcement Learning
Mutual Information Regularized Offline Reinforcement Learning
Xiao Ma*, Bingyi Kang*, Zhongwen Xu, Min Lin, Zhongwen Xu, Shuicheng Yan (*equal contributions)
Conference on Neural Information Processing Systems (NeurIPS), 2023
Imitation Learning via Differentiable Physics
Imitation Learning via Differentiable Physics
Computer Vision and Pattern Recognition (CVPR), 2023
DaxBench
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)
DiffMimic
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
gdoom
Learning Latent Graph Dynamics for Deformable Object Manipulation
International Conference on Robotics and Automation (ICRA), 2022
gdoom
Ab Initio Particle-based Object Manipulation
Robotics: Science and Systems (RSS), 2021
star
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
Cunjun Yu*, Xiao Ma*, Jiawei Ren, Haiyu Zhao, Shuai Yi (* equal contributions)
European Conference on Computer Vision (ECCV), 2020
dan
Differentiable Algorithm Networks for Composable Robot Learning
Robotics: Science and Systems (RSS), 2019 (best system paper finalist & best student paper finalist)