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About

Rico Jonschkowski is a robotics and machine learning researcher whose work sits at the intersection of state representation learning, probabilistic filtering, and real-world robotic systems. He has made influential contributions to how robots learn structured, physically meaningful representations of their environments, pioneering the use of robotic priors — constraints inspired by physics — to guide representation learning more efficiently than purely data-driven approaches. His 2015 paper on learning state representations with robotic priors has accumulated 160 citations and remains a foundational reference in the field. Jonschkowski gained broader recognition through his team's winning entry to the Amazon Picking Challenge, with related publications collectively drawing over 200 citations and offering practical lessons on designing modular, real-world robotic systems. His work on KeyPose (131 citations) extended 3D object pose estimation to transparent objects using multi-view RGB images — a notoriously difficult problem for depth-based systems. He has also advanced differentiable particle filters for end-to-end state estimation and contributed to large-scale multi-task robotic reinforcement learning through MT-Opt. His Distracting Control Suite benchmark highlights challenges in transferring reinforcement learning from simulation to realistic visual settings, reflecting his sustained commitment to bridging the gap between research and deployable robotic intelligence.