I am a final-year Robotics & Mechatronics Engineering student at the University of Dhaka. My research builds adaptive neural controllers that let robots keep moving under uncertain, deformable contact; currently, learning policies that recover a Mars rover's mobility when its wheels become entrapped in granular terrain, where classical control fails under slip.
Methodologically I focus on reward shaping with safety-gated constraints, for instance, yaw-projected rewards with entrapment-gated slip penalties, and on Sim2Real transfer through domain randomization and curriculum learning. More broadly, I am drawn to safe and preference-driven reinforcement learning, learning-based control, and neuro-robotics for movement: robots that move robustly in the physical world and learn from interaction in safe, sample-efficient, human-aligned ways.
Wheel entrapment in loose regolith has crippled real planetary missions (e.g., NASA's Spirit rover). We cast recovery as a reinforcement-learning control problem in deformable granular dynamics, learning policies that exploit terrain feedback to regain mobility, bridging RL, locomotion, and safe control under uncertainty.
A 31.8 × 54 m digital-twin warehouse benchmarking PPO, SAC, and TD3 for autonomous navigation on a Clearpath Jackal under realistic LiDAR and localization noise.
A vision-guided pick-and-place pipeline transferred from simulation to physical hardware, with IK solvers and collision-aware trajectory optimization for closed-loop grasping.
SLAM mapping, AMCL localization, and hybrid A*/DWA planning for dynamic obstacle avoidance, deployed on a real differential-drive robot.
A from-scratch STM32 flight controller with cascaded PID attitude/rate loops and IMU sensor fusion, achieving stable autonomous flight.