Max Robotics — Global Service Robot Directory + US Certification

About

Niko Kleer is a robotics researcher whose work lies at the intersection of human-robot collaboration, autonomous assistance, and surgical robotics. His research focuses on enabling robots to understand and adapt to human intent, whether through anthropomorphic grasping, multimodal interaction, or surgeon-in-the-loop systems. Kleer’s most cited paper, "Leveraging Publicly Available Textual Object Descriptions for Anthropomorphic Robotic Grasp Predictions" (2022, 4 citations), tackles the challenge of robotic grasp planning by using natural language descriptions to predict suitable poses for anthropomorphic end-effectors. In "Towards a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice" (2024, 3 citations), he explores reinforcement and imitation learning to create robotic systems that adapt to individual surgeons’ preferences, moving beyond one-size-fits-all surgical assistants. His work on "A Multimodal Teach-in Approach to the Pick-and-Place Problem" (2023, 3 citations) advances human-robot collaboration by integrating speech, gestures, and gaze for more natural task instruction. Kleer also addresses real-world applications in "Towards Remote Expert Supported Autonomous Assistant Robots in Shopping Environments" (2024, 2 citations), where he combines autonomy with human oversight to handle complex customer requests. Through these contributions, Kleer is shaping a future where robots are not just tools, but intuitive partners in surgery, industry, and daily life.