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About

Michel Breyer is a robotics researcher whose work sits at the intersection of robot perception, grasping, and autonomous manipulation in unstructured environments. His research addresses one of robotics' most persistent challenges: enabling robots to reliably find, perceive, and grasp objects amid clutter and uncertainty. Breyer's most influential contribution, "Object Finding in Cluttered Scenes Using Interactive Perception" (64 citations), exemplifies his signature approach of coupling perception with physical action — using each to inform the other in a closed feedback loop. This philosophy carries through his widely cited work on deep reinforcement learning for object picking (55 citations) and his Volumetric Grasping Network (44 citations), which achieves real-time 6-DOF grasp detection for previously unseen objects. Together, these papers have established him as a meaningful voice in data-driven robot manipulation. Beyond grasping, Breyer has tackled broader autonomy challenges, including next-best-view planning, mobile manipulation in domestic settings, and hierarchical POMDP-based object search. His exploration of sim-to-real transfer and sensor calibration via reinforcement learning further demonstrates the breadth of his technical contributions. With over 240 total citations across a focused body of work, Breyer represents an emerging researcher making substantive progress toward robots that can operate capably and flexibly in the real world.