Max Yuan

highlights

• Built a custom ManagerBasedRLEnv for flat-terrain Go2 locomotion: 48-dim observations, 12-DOF joint-position actions, and a custom reward stack.
• Designed a sigmoid curriculum that ramps up the foot-lift reward over training, so the policy learns to balance before the reward starts shaping its gait.
• Early policies learned to crawl; a custom height reward fixed it, and squared action-rate penalties keep joint motion smooth.
• Next: get-up recovery policies and domain randomization for sim-to-real transfer.

highlights

• Integrated slam_toolbox mapping, AMCL localization, and Nav2 with a Regulated Pure Pursuit controller, debugging in RViz.
• Built the full TF tree (map → odom → chassis_link → sensors) with Isaac Sim action graphs; debugged QoS mismatches, sensor-rate bottlenecks, and frame-authority conflicts.
• Traced a warped SLAM map to LiDAR scans lagging odometry during turns, and fixed it by raising the RTX LiDAR scan rate to 20 Hz.

highlights

• A Groq LLM turns a plain-language request into a list of robot actions.
• A homography maps YOLOv8 detections from pixels to positions on the bench, so tools can go anywhere instead of at fixed spots.
• An inverse kinematics solver turns that position into the joint angles needed to reach and grab the tool.

highlights

• Tuned three PID controllers (x, y, heading) for point-to-point autonomous pathing, turning the integral term on only within 10–20 cm of the target so moves finish without overshoot.
• Fused two-wheel odometry with IMU heading, rotating robot-frame motion into field coordinates.
• Automated sample pickup with OpenCV and a custom-trained CNN that estimates each sample's distance and orientation.