Robotics · Vision · Learning
Ran Cheng
Research Executive Director·Primebot
I lead foundation-model research on robot learning — and I write deep, interactive explainers about how these systems work.
- Foundation models for robot learning
- Interfaces for efficiently distilling human skill into robots
About
I'm the Research Executive Director at Primebot, where I lead a team building robotics foundation models — VLA, world models, reinforcement learning powered by universal reward functions, and memory & continual learning. Previously, I built Ant Group's robot learning stack from the ground up, from data collection and pretraining infrastructure to post-training and large-scale benchmarking; before that, I led an R&D department at Midea and scaled robotics products to over one million production units; and at Huawei Noah's Ark Lab, I built scene reconstruction and world-model systems for autonomous driving. I hold an M.Sc. from McGill University's Center for Intelligent Machines (advised by Gregory Dudek and David Meger) and a B.S. from Tongji University.
Writing
all posts →Research
publications →Selected work on vision-language-action models and robot-learning policies for manipulation, world models and embodied-AI benchmarks, and — from earlier — LiDAR perception, semantic scene completion, visual odometry, and monocular SLAM — see the publications page.