I work on robot learning at World Labs. I received my Bachelor's degree in Applied Math and CS from Columbia University, where I was fortunate to be mentored by Yunzhu Li, Antonio Loquercio, and Brian Plancher.
My research sits at the intersection of robotics, computer vision, and machine learning, with a focus on understanding the role of world models in data-driven robotics pipelines to equip robots with more robust, capable, and efficient physical and perceptual capabilities. I aim to use world models to scale robotics progress at the speed of compute rather than real-world clock time.
If you would like to collaborate, feel free to reach out!
Updates
- Sep 2026 Residual Copilot accepted to CoRL 2026!
- May 2026 Graduated with my Bachelor's degree in Applied Math at Columbia.
- Jan 2026 Real2sim Eval and TAG-K accepted to ICRA 2026!
- Sep 2025 Interned as a Robotics Researcher at SceniX during summer.
Research
(* indicates equal contribution)
Shuo Sha,
Yixuan Wang,
Binghao Huang,
Antonio Loquercio,
Yunzhu Li
CoRL 2026,
[website],
[paper],
[sim code],
[deploy code]
TL;DR: We present a shared autonomy framework for reliable teleoperation by learning a residual copilot that provides low-level assistance.
Kaifeng Zhang*,
Shuo Sha*,
Hanxiao Jiang,
Matt Loper,
Jay Song,
Zhuo Xu,
Xiaochen Hu,
Changxi Zheng,
Yunzhu Li
ICRA 2026,
[website],
[paper],
[code]
CVPR 2026 4DV Workshop (Oral Presentation)
TL;DR: We propose a framework for robot policy evaluation in simulation, using Gaussian Splatting for rendering and soft-body digital twin for dynamics.
Shuo Sha,
Anupam Bhakta*,
Zhenyuan Jiang*,
Kevin Qiu*,
Ishaan Mahajan,
Gabriel Bravo,
Brian Plancher
ICRA 2026,
[website],
[paper],
[code]
TL;DR: We introduce TAG-K, a lightweight Kaczmarz variant combining greedy row selection and tail averaging for fast online inertial parameter estimation.
Shuo Sha
Journal of Mathematics Research 2023,
[paper]
TL;DR: We present a well-posed variational formulation and finite element approximation for time-harmonic 2D Maxwell's equations.
