Physical AI · Humanoid Robotics · VLA Models · ROS 2 · MPC · Sensor Fusion · Autonomous Navigation
I work on robotics and autonomous systems, with a current focus on Physical AI for industrial humanoid robots.
My background combines optimal control, state estimation, robot navigation, ROS 2 systems, and machine learning, with completed work on UAV autonomy and current research/development on Vision-Language-Action systems for humanoid robots.
Accepted at IEEE ICRA 2026 Xplore Workshop — Oral Presentation
This work presents a robot-agnostic autonomous navigation framework that combines:
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LiDAR-based Gaussian occupancy representation
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A* rolling-horizon planning
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Nonlinear Model Predictive Control
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Bayesian Optimization for MPC parameter tuning
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Simulation-to-real deployment on a Unitree Go2 quadruped robot
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Physical AI & humanoid robotics
- Vision-Language-Action systems for industrial humanoids
- Task grounding, robot reasoning, and real-world deployment
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Autonomous navigation
- MPC-based planning and control
- Bayesian Optimization for controller tuning
- Sim-to-real validation on legged robots
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UAV trajectory planning & control
- Completed work on MPC-based quadrotor trajectory generation
- Constraint-aware planning for real-world drone dynamics
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Sensor fusion & state estimation for UAV's
- Extended Kalman Filters
- Multi-sensor fusion with IMU, GPS, magnetometer, and barometer data
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ROS 2 & simulation
- ROS 2 Humble development
- Gazebo-based testing, simulation, and integration
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Visual SLAM & perception
- Stereo / RGB-D visual odometry
- Mapping and pose-estimation pipelines
Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation
Robot-agnostic autonomous navigation framework using rolling-horizon planning, nonlinear MPC, Bayesian Optimization, and sim-to-real validation on a Unitree Go2 quadruped robot. The same pipeline is also testd into the unitree G1 robot
➡️ https://github.com/talos-robotics-ai/Go2_navigation
➡️ https://github.com/Relo02/Navigation
Completed UAV trajectory planning project using MPC-based approaches for quadrotor navigation with ROS 2, Gazebo, and PX4.
➡️ https://github.com/Relo02/Drone-optimal-trajectory
Completed EKF-based sensor fusion framework for quadrotor state estimation using multiple onboard sensors.
➡️ https://github.com/Relo02/Quadcopter-Sensor-Fusion
Completed ROS 2 Humble simulation environment for UAV control-system experimentation, obstacle awareness, and smart landing procedures.
➡️ https://github.com/FALCOdrone/Ros-2-Environment
Academic and practical work on neural networks and deep learning concepts.
➡️ https://github.com/Relo02/Artificial-neural-networks-and-deep-learning-
Visual SLAM and VIO pipelines using stereo / RGB-D sensors for pose estimation and mapping.
➡️ https://github.com/palingenesys/Visual-Slam
- Robotics: ROS 2 Humble, Gazebo, PX4, autonomous navigation
- Physical AI: humanoid robotics, VLA systems, Reinforcement Learning, sim-to-real deployment
- Control & Planning: MPC, nonlinear MPC, optimal control, trajectory optimization
- Optimization: Bayesian Optimization, Gaussian Processes, TPE-based tuning
- Estimation & Perception: EKF, sensor fusion, Visual SLAM, VIO
- Sensors: IMU, GPS, LiDAR, stereo cameras, RGB-D cameras
- ML/DL: PyTorch, neural networks, deep learning
- Development: Python, C++, MATLAB, Docker, Linux, Arduino
⭐ I enjoy turning robotic systems, Physical AI, and complex autonomous software into real-world robotic capabilities.
