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About

Andreas ten Pas is a robotics researcher whose work sits at the intersection of robotic grasping, 3D perception, and manipulation in unstructured environments. He has made significant contributions to the problem of grasp pose detection, developing geometric and learning-based methods that enable robots to identify reliable grasp configurations directly from 3D point cloud data — work that has collectively accumulated hundreds of citations and helped define best practices in the field. His most cited contribution, a 2014 study on GelSight tactile sensing for small-part manipulation (254 citations), demonstrated how high-resolution tactile feedback could enable precise localization and control during delicate assembly tasks. Alongside this, his series of papers on geometry-based grasp detection in point clouds (157 and 45 citations) established principled frameworks for identifying 6-DOF grasp candidates on novel objects in clutter. He further advanced the field by bridging simulation and reality, training visuomotor controllers on synthetic depth images to enable real-world grasping (100 citations), and tackling the challenge of searching for target objects in cluttered, partially observable environments (68 citations). His development of the low-cost Baxter Easyhand also reflects a commitment to accessible, practical robotics hardware. Collectively, ten Pas's research offers foundational tools for building robots capable of operating reliably in complex, everyday settings.