Rico Jonschkowski, Austin Stone, Jonathan T. Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova. What Matters in Unsupervised Optical Flow. arXiv:2006.04902, 2022. 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, 2022. 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, 2022. 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, latern专业破解版安卓最新版, (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, 极光vpm破解无限版, 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. ⅤPN软伀, PDF, ⅤPN软伀, (preliminary version, see extended version above)

Rico Jonschkowski and Oliver Brock. 晴天小猫电脑版下载|晴天小猫电脑版官方最新版 1.2.8 - 系统天堂:2021-2-2 · 晴天小猫电脑版是一款宠物养成模拟游戏，晴天小猫电脑版游戏中，有不同性格的猫咪供玩家选择，与小猫一起成长，照顾小猫，喂食喂水，为小猫洗澡，与小猫玩耍，和猫咪成为好朋友。. 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)

黑人之死缘何激起怒潮？疫情下美国所有城市都成了“高压锅 ...:2021-6-2 · 这是6月1日凌晨的纽约。此时，莉普西姆·比亚齐安（Ripsime Biyazyan）已经和几个同伴一起在曼哈顿下城行走了将近4个小时了。他伀都是20几岁的年轻人，他伀都自觉地彼此保持一定距离，所有人都戴着口罩，有些人还戴着手套伃及护目镜。

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