Xiao Ma (马骁)

I am a research scientist at SEA AI Lab. Prior to this, I was a 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, 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.

Email  /  CV  /  Google Scholar  /  Semantic Scholar  /  Twitter  /  Github

- [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!

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.

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.

We introduce CVRL, contrastive model-based reinforcement learning for complex observations. Different from standard generative models, CVRL learns a contrastive latent world model and significantly improves the robustness against complex observations.

Our key observation is that softmax is biased under the long-tailed distribution. BALMS provides a mathematically unbiased gradient estimate for long-tailed distributions and applies meta-learning to further improve the data sampling process.

We introduce STAR, the first transformer-based pedestrian trajectory predictor. STAR generalizes the Transformers into spatio-temporal graphs and significantly improves the trajectory prediction accuracy (2x).

We introduce DPFRL for reinforcement learning for complex partial observations. DPFRL encodes a discriminative particle algorithm as a differentiable computational graph in neural networks which improves the belief tracking.

We introduce PF-RNNs for general sequence prediction under uncertainty. PF-RNNs encodes a differentiable particle filter algorithm with standard RNNs and improves the general sequence prediction performance.

A DAN is composed of neural network modules, each encoding a differentiable algorithm and an associated model; and it is trained end-to-end from data. The algorithms and models act as structural priors to reduce the data requirements for learning; end-to-end learning allows the modules to adapt to one another and compensate for imperfect models and algorithms.

Stolen from Jon Barron