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Volodymyr Kalinin
Intern AI & Robotics Engineer specializing in reinforcement learning and computer vision
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About
Robotics/AI engineer focused on multi-agent reinforcement learning for Crazyflie drones, enabling coordination via implicit motion-based communication and a stabilizing FSM layer; reported 98.5% sim and 92% real-world behavior-recognition accuracy. Also built a modular ROS 2 wall-following system (custom nodes/services/actions) and a Raspberry Pi + OpenCV stereo-vision walking robot, emphasizing rigorous logging, stress testing, and sim-to-real deployment.
Experience
Education
Key Strengths
- Built sim-to-real multi-agent drone coordination using implicit motion-based signaling (RL + classifier + FSM)
- Achieved 98.5% accuracy in simulation and 92% in real-world drone tests
- Designed modular ROS 2 architecture with custom nodes/services/actions and separate packages for interfaces vs control logic
- Methodical real-time debugging via perception/control decoupling and logging (stereo frames, disparity maps) to isolate root causes
- Optimized vision pipeline for on-robot performance (reduced resolution, ROI depth computation, temporal smoothing) to stabilize gait
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