Max Robotics — Global Service Robot Directory + US Certification

About

Samyakh Tukra is a researcher at the intersection of computer vision, deep reinforcement learning, and surgical robotics. His most impactful contribution is **VisionBlender** (62 citations), an open-source tool that efficiently generates synthetic endoscopic datasets for training and testing computer vision algorithms in robotic surgery—addressing a critical bottleneck in the field. He also co-organized the **SurgT challenge** (23 citations), establishing a benchmark for soft-tissue tracking in robotic surgery. Tukra has advanced the explainability of deep reinforcement learning agents trained with domain randomisation, publishing two related works (21 and 7 citations) that analyse how these agents learn and transfer from simulation to real-world surgical tasks. His broader work includes surveys on AI in surgical robotics and meta-learning. By providing essential tools, benchmarks, and analytical frameworks, Tukra’s research directly supports the development of more robust, interpretable, and deployable AI systems for minimally invasive surgery.