Kulunu Samarawickrama

Tampere University, Institute of Automation

Papers

Total Citations

H-Index

About

Kulunu Samarawickrama is a robotics researcher focused on advancing human–robot collaboration and autonomous manipulation in industrial settings. His work centers on developing perception and interaction systems that enable robots to work safely and effectively alongside humans. His most cited paper, "Sensor-based human–robot collaboration for industrial tasks" (2023, 39 citations), addresses the challenge of designing interaction modalities that adapt to the constraints of industrial environments, such as variable lighting, to ensure seamless collaboration. In "Automatic Dataset Generation From CAD for Vision-Based Grasping" (2021, 7 citations), he proposed a method to automatically generate training data from CAD models, reducing the need for manual annotation in deep learning-based robotic grasping. His recent work, "6D Assembly Pose Estimation by Point Cloud Registration for Robotic Manipulation" (2024, 2 citations), tackles precise pose estimation for complex assembly tasks, a critical step toward dexterous robotic manipulation. Samarawickrama’s contributions bridge simulation and real-world application, offering scalable solutions for industrial automation. His research is particularly valuable for students and engineers interested in the intersection of computer vision, sensor fusion, and collaborative robotics.