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About

Andreas Theissler is a leading researcher at the intersection of robotic manipulation and intelligent sensing, whose work is advancing the dexterity and autonomy of robotic systems. His primary research areas include haptic sensing, force/torque control, and deep reinforcement learning for contact-rich tasks. Theissler’s major contributions span from developing fingertip 6-axis force/torque sensors that enable texture recognition in robotic hands—a breakthrough for humanoid and industrial manipulators—to applying force-based deep Q-learning for high-precision assembly operations, such as peg-in-hole insertions with tight clearances. His work on simulated surgical palpation, with 5 citations, directly addresses the critical challenge of detecting hard inclusions in soft tissue during robot-assisted surgery, bridging haptic perception and clinical application. With over 19 citations across his most-cited papers, Theissler’s research has tangible impact in manufacturing automation and medical robotics. Notably, his 2023 study on robotic peg-in-hole insertion demonstrates how reinforcement learning can master complex assembly skills, while his 2022 work on visual detection of tiny, transparent objects tackles the intricate task of autonomous pick-and-place for miniature sensor assembly. Theissler’s interdisciplinary approach continues to push the boundaries of what robots can feel and do.