Lintao Zheng

I am currently a second-year master's student in Robotics at the University of Pennsylvania (UPenn).

My research experience includes contributing to robot planning and control under uncertainty at the UPenn Figueroa Robotics Lab with Prof. Nadia Figueroa, and developing a teleoperation forklift system at the Penn xLab with Prof. Rahul Mangharam.

Previously, I also gained significant experience in sensor fusion and autonomous robot vehicles, including developing a Doppler radar-based speed detection system and line-following robot vehicles during my undergraduate studies at the University of Nottingham Ningbo China (UNNC), as well as designing and building a teleoperated robot with multimodal sensors at UPenn.

My primary research interests involve robot learning and AI, especially in perception-based reinforcement learning, imitation learning, and uncertainty-aware learning frameworks, aiming to enable robots to autonomously acquire adaptive, robust, and intelligent behaviors through embodied interactions in complex real-world scenarios.

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1. Integrated perception uncertainty into the temporal-logic planning and control model for interactive robotic tasks, improving robustness under noisy observations.
2. Implemented a YOLO-MPPI pipeline that fuses object detections with sampling-based control for robust target tracking.
3. Added MPPI obstacle-avoidance policies informed by a human predictive model, enabling anticipation of human motion and safe, real-time navigation.

1. Utilized the CAN protocol to establish communication between the Jetson and the forklift.
2. Translated joystick inputs on the Jetson into actionable commands for forklift control.
3. Designed and fabricated a custom base using 3D printing and laser cutting to securely mount the Jetson, sensors, and power system.

1. Developed a 3-DOF teleoperated robot using Itsy Bitsy and Arduino for precise servo control.
2. Designed the Waldo system for smooth teleoperation, including servo selection and custom coding.
3. Led the full development cycle across mechanical, circuit, and software design.
4. Integrated sensors (ultrasonic, IR, force) for obstacle detection and environmental interaction.
5. Optimized control algorithms with PID and custom logic to boost efficiency and accuracy.