Max Schwarz
Papers
41
Total Citations
1,607
H-Index
20
About
Max Schwarz is a robotics researcher whose work sits at the intersection of deep learning-based perception, autonomous manipulation, and mobile disaster-response robotics. His most influential contributions center on applying convolutional neural networks to RGB-D sensing: his 2015 paper on transfer learning for object recognition and pose estimation has accumulated over 320 citations, demonstrating that pre-trained CNN features can dramatically reduce the cost of dataset creation for manipulation robots. Building on this foundation, his 2017 work on object detection and semantic segmentation in cluttered environments (165 citations) advanced autonomous robotic grasping in complex, occluded scenes. Schwarz has also made significant strides in disaster-response robotics through the NimbRo team, contributing to the celebrated Momaro platform used in the DARPA Robotics Challenge (146 citations) and the CENTAURO system. His research extends into immersive teleoperation, with a VR-based telepresence system (111 citations) and the NimbRo Avatar featuring force-feedback telemanipulation earning notable recognition, including success in the ANA Avatar XPRIZE competition. Across competitions like the Amazon Robotics Challenge and DLR SpaceBot Cup, Schwarz has consistently translated research into competitive, real-world robotic systems, cementing his reputation as a versatile and impactful figure in modern robotics research.
Research Focus
Key Achievements
Top Papers
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- 4A VR System for Immersive Teleoperation and Live Exploration with a Mobile Robot111 citations · 2019
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- 8Hybrid driving-stepping locomotion with the wheeled-legged robot Momaro61 citations · 2016
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