Thomas Lips

Papers

Total Citations

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About

Thomas Lips is a leading researcher in robotic cloth manipulation, a field critical for developing assistive robots capable of household tasks like washing, folding, and ironing. His work tackles the fundamental challenge of deformable object manipulation, where cloth’s infinite degrees of freedom and self-occlusions make state estimation notoriously difficult. Lips’ major contribution is pioneering the use of synthetic data and learned keypoints to bridge the sim-to-real gap. By training convolutional neural networks to detect semantic keypoints—such as corners or edges—on cloth, his systems can robustly infer folding actions without needing a full cloth state, dramatically improving generalization across different fabrics and configurations. His impact is demonstrated through his competition-winning system, which secured first place in the folding track at the IROS 2022 Cloth Manipulation and Perception Competition, and again at ICRA 2023. This work, detailed in a comprehensive analysis paper, provides a blueprint for robust, real-world cloth folding. With over 23 citations across his top papers, Lips is shaping the future of domestic robotics, proving that synthetic data and keypoint detection can overcome the long-standing barriers to practical, assistive cloth manipulation.