SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution

Soon-to-be Ph.D. graduate in Robotics and AI researcher specializing in robotic mamipulation. Skilled in complex simulation design using Isaac Sim and Isaac Lab, and sim-to-real transfer for unstructured, real-world orchard environments. Expertise in creating novel RL algorithms, applying Deep Reinforcement Learning and Imitation Learning to deploy robust manipulation policies on physical robotic platforms. Recognized for research contributions in top-tier conferences (ICRA, CVPR, AAAI), with proven experience in synthetic data generation and training RL, generative AI, and Large Language Models (LLMs).