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Yutao Cao
Junior Machine Learning Engineer specializing in computer vision and 3D/robotics research
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About
Robotics software candidate focused on simulation-to-learning workflows, building a novel-view-synthesis pipeline (USCiLab3D) with a multi-modal diffusion model and a LiDAR-driven, geometry-aware sampling strategy for selecting overlapping reference views across trajectories/seasons. Also designed coordinated motion planning for two Ridgeback-Franka robots in Isaac Lab for a non-prehensile collaborative task, augmenting controller limitations with RL-based self-collision termination states.
Experience
Education
Key Strengths
- Geometry-aware training data sampling using LiDAR reverse projection for view overlap
- Scaling training for a multi-modal diffusion model in a large dataset simulation pipeline
- Bridging perception/simulation/training pipelines with ROS and Isaac Lab
- Designed coordinated motion planning for two Ridgeback-Franka robots for non-prehensile collaboration using differential IK
- Improved robustness by addressing IK/controller limitations with RL-based self-collision termination states
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