Kalie Ching

Hi, I'm Kalie. I am extremely passionate about my work in tactile-aware manipulation policies for dexterous hands and loco-manipulation policies for humanoids at the Bosch Center for AI and Carnegie Mellon University.

Previously, I was a research engineer working on training reinforcement learning policies and visuomotor manipulation policies at the Robotics and AI Institute (formerly Boston Dynamics AI Institute) to teach robots how to perform new skills efficiently and safely. I have also worked on autonomous cars, robots on the International Space Station, and large-scale databases.

Other interests: fashion, character design, nutrition, ballroom

All work

EgoX: Egocentric Cross-Embodiment Manipulation with Embodiment Dreaming ↗

Yaru Niu*, Binghong Chen*, Changwei Yao*, Shangtao Li*, Zhenlong Fang, Revanth Krishna Senthilkumaran, Shuai Zhou, Yuemin Mao, Kalie Ching, Po-Ju Chao, Yudong Luo, Peiqi Yu, Heng Zhang, Hao Zhang, Bingqing Chen, Chen Qiu, Eric H. Tseng, Changliu Liu, Jonathan Francis, Ding Zhao

Preprint, 2026 · * Equal contribution

EgoX learns shared manipulation skills from egocentric human and robot demonstrations, then adapts them to different robot bodies.

Paper ↗ Project page ↗
A humanoid robot demonstrating manipulation tasks for EgoX.

Show, Don’t Tell: Detecting Novel Objects by Watching Human Videos ↗

James Akl, Jose Nicolas Avendano Arbelaez, James Barabas, Jennifer L. Barry, Kalie Ching, Noam Eshed, Jiahui Fu, Michel Hidalgo, Andrew Hoelscher, Tushar Kusnur, Andrew Messing, Zachary Nagler, Brian Okorn, Mauro Passerino, Tim J. Perkins, Eric Rosen, Ankit Shah, Tanmay Shankar, Scott Shaw

Preprint, 2026

Show, Don’t Tell lets a robot detect new objects from a single human demonstration, building its own labeled dataset and training a task-specific detector without language prompts.

Paper ↗ Blog post ↗
Research demonstration from the Robotics and AI Institute’s Show, Don’t Tell project.
Image: Robotics and AI Institute.