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Estimation, Search, & Planning Research Group

Welcome to the Estimation, Search, and Planning (ESP) research group at the Oxford Robotics Institute (ORI).

ESP is led by Dr. Jonathan Gammell and is focused on studying and understating the fundamental problems of robotics as motivated by real-world problems.

Please visit the group's webpage to find out more about our individual research, including our publicly available available code and datasets.

Jonathan has been working with @vnhartmann to extend @marlinpolo's work on effort-informed planning to multiquery problems. Their Effort Informed Roadmaps (EIRM*) can solve individual problems an order-of-magnitude faster and has been submitted to ISRR… https://t.co/9TGlWs0Hwc

RT @oxfordrobots: “System Team Brown-bag sessions” with a focus on “robotics engineering” and “robots in the real world”. Our speaker Mart…

RT @oxfordrobots: Launching tomorrow is the “System Team Brown-bag sessions” with a focus on “robotics engineering” and “robots in the real…

Congratulations to @marlinpolo for defending his D.Phil. on Leveraging Multiple Sources of Information to Search Continuous Spaces. Many thanks to his examiners Prof. Wheeler Ruml and Prof. Nick Hawes @hawesie for their time and effort ... https://t.co/Lkdo4zNB87

Come hear the latest from @NASAJPL about @NASAPersevere, @MarsCuriosity, and so much more. This is going to be a great talk. https://t.co/WLBw8jrGUL

Kevin has submitted a journal paper on Multimotion Visual Odometry (MVO) to IJRR. This is his definitive version of the work we're doing in ESP on measuring moving scenes from a dynamic camera. You learn more from the trailer video and multimedia… https://t.co/5zz6LmOXSt

RT @oxfordrobots: 2 weeks to go! 📢 Register now for the second seminar featuring Michiel van de Panne from @UBC at https://t.co/2jxapqgfEa…

Marlin @marlinpolo has submitted a journal paper on asymmetric bidirectional planning to IJRR. We're happy to provide its algorithms, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*), as open-source OMPL code. You can find out more ... https://t.co/2visqMWLM9

Happy to be part of this exciting week-long event. https://t.co/vOVKnxSkd1