Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova. What Matters in Unsupervised Optical Flow. arXiv:2006.04902, 2020. Code, PDF, BibTeX

TL;DR: A thorough study into the core components of unsupervised optical flow produces in a simple method that sets a new state of the art

Rico Jonschkowski and Austin Stone. Towards Object Detection from Motion. NeurIPS Workshop on Robot Learning: Control and Interaction in the Real World, 2019. PDF, BibTeX

TL;DR: A method for learning to detect objects from a few minutes of video without image annotations

Peter Karkus, Anelia Angelova, Vincent Vanhoucke, and Rico Jonschkowski. Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization. arXiv:2005.09530, 2020. PDF, BibTeX

TL;DR: combining spatial structure and end-to-end learning for mapping and localization

Rico Jonschkowski, Divyam Rastogi, and Oliver Brock. Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors. Proceedings of Robotics: Science and Systems, 2018. PDF, BibTeX

TL;DR: the particle filter structure encoded in a recurrent deep network

Rico Jonschkowski and Oliver Brock. End-To-End Learnable Histogram Filters. Workshop on Deep Learning for Action and Interaction at NeurIPS, 2016. PDF, BibTeX, (extension of the paper below)

Rico Jonschkowski and Oliver Brock. Towards Combining Robotic Algorithms and Machine Learning: End-To-End Learnable Histogram Filters. Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics at IROS, 2016. PDF, BibTeX, (preliminary version, see extended version above)

TL;DR: a way to combine the algorithmic structure of Bayes filters with the end-to-end learnability of neural networks

Rico Jonschkowski, Roland Hafner, Jonathan Scholz, and Martin Riedmiller. PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations. New Frontiers for Deep Learning in Robotics Workshop at RSS, 2017. PDF, BibTeX, Paper Award winner

TL;DR: unsupervised learning of where things are and how they are moving

Sebastian Höfer, Antonin Raffin, Rico Jonschkowski, Oliver Brock, and Freek Stulp. Unsupervised Learning of State Representations for Multiple Tasks. Workshop on Deep Learning for Action and Interaction at NeurIPS, 2016. PDF, BibTeX

TL;DR: a learning method for automatic detection of multiple reinforcement tasks and extraction of state representations from raw observations

Rico Jonschkowski and Oliver Brock. Learning State Representations with Robotic Priors. Autonomous Robots. Springer US 39(3):407-428, 2015. Code, Video of Robot Experiment, PDF, BibTeX, (extension of the two papers below)

Rico Jonschkowski and Oliver Brock. State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction. Proceedings of Robotics: Science and Systems, 2014. Talk, PDF, BibTeX, (preliminary version, see extended version above)

Rico Jonschkowski and Oliver Brock. Learning Task-Specific State Representations by Maximizing Slowness and Predictability. Proceedings of the 6th International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems (ERLARS), 2013. PDF, BibTeX, (preliminary version, see extended version above)

TL;DR: state representations can be learned from raw sensory input by making these representations consistent with prior knowledge about interactions governed by physics = robotic priors

Rico Jonschkowski, Clemens Eppner*, Sebastian Höfer*, Roberto Martín-Martín*, and Oliver Brock. Probabilistic Multi-Class Segmentation for the Amazon Picking Challenge. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016. Code, PDF, BibTeX, IROS Best Paper Award Finalist

TL;DR: in-depth analysis of the object-segmentation method used in our winning entry to the 2015 Amazon picking challenge with lessons that should be useful towards more generic robotic perception

Clemens Eppner*, Sebastian Höfer*, Rico Jonschkowski*, Roberto Martín-Martín*, Arne Sieverling*, Vincent Wall* and Oliver Brock. Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems. Proceedings of Robotics: Science and Systems, 2016. Talk, PDF, BibTeX, RSS Best Systems Paper Award winner

TL;DR: four aspects that improve our exploration and understanding of how to build robotic systems: modularity vs. integration, computation vs. embodiment, planning vs. feedback, generality vs. assumptions