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Yunxi Zhu
Entry-level Robotics & Automation Engineer specializing in robot learning and manufacturing automation
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Robotics software engineer focused on real-time teleoperation and high-quality robot-learning data pipelines, including synchronized multimodal sensing (RGB-D, tactile, joint states) for Diffusion Policy training on a bimanual ALOHA robot. Strong ROS practitioner who debugs real-time control issues with ROS tooling and builds simulation environments in Isaac Lab and PyBullet; also packages data-collection stacks with Docker.
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Key Strengths
- Multimodal sensing synchronization for robot learning datasets (RGB-D, tactile, joint data)
- Built shared-memory, timestamped buffering with interpolation and strict write ordering
- Developed visualization tools to validate sensor alignment (contact events vs camera frames)
- ROS integration and real-time control pipeline development (services, pub/sub, tf, state publishers)
- Methodical real-time debugging using ROS tooling (rqt_graph, rostopic hz) and joint-command logging
- Improved teleoperation smoothness by fixing timing mismatch between IK output and motor command node
- Simulation-based training environments in Isaac Lab (bimanual pick-and-place) and PyBullet (biped walking)
- Packaged teleoperation/data-collection stack with Docker
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