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About

Stephen Laws is a leading researcher at the intersection of robotics, computer vision, and surgical assistance. His work focuses on advancing robotic systems for orthopaedic and minimally invasive surgery, with key contributions in force sensing, tool localisation, and automated tissue classification. Laws is best known for his comprehensive review on six-axis force/torque sensors for robotics applications, which has garnered over 130 citations and serves as a foundational reference for researchers developing haptic feedback and interaction control in robotic systems. He also introduced SimPS-Net, a novel deep learning architecture for simultaneous pose estimation and segmentation of surgical tools, enabling safer and more precise robot-assisted surgery. His pioneering studies on markerless orthopaedic robotic assistance explore diffuse laser reflectivity for automated bone segmentation, aiming to eliminate the need for fiducial markers and streamline surgical workflows. More recently, Laws has advanced real-time active constraint generation for collaborative surgical robots, integrating 3D detection networks with control algorithms to enforce safe tool boundaries. Through his work, Laws is shaping the next generation of intelligent, context-aware surgical robots that enhance both autonomy and safety in the operating room.