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There are two weeks left to apply for our Lead Software and Systems post. The perfect opportunity for anyone with the desire and know-how to help robots fulfil their potential!

Our work on adaptive control and state-estimation recently got accepted at #ICRA2021! Paper: arxiv.org/abs/2005.05894 Repo and videos: github.com/MoBaioumy/acti… With Paul Duckworth, Bruno Lacerda and Nick Hawes. This work was supported by @ORCA_Hub, RAIN hub and Oxford Robotics Institute.

Amazing team to be part of.

Really excited to be part of the Astronaut Health Team at FDL.AI 2021!! Stay tuned for exciting work on causal inference and out-of-distribution/domain transfer learning

On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference deepai.org/publication/on… by Mohamed Baioumy et al. including Paul Duckworth #ArtificialIntelligence #MarkovDecisionProcess

The main deadline for DPhil/PhD applications to Engineering Science, Oxford is in a month (3 Dec 2021). Make sure to have your application in for this date to be eligible for scholarships including for Oxford Robotics Institute. ox.ac.uk/admissions/gra…

Pleasantly surprised that our paper received runner-up for the best paper award at ICAPS Conference! The paper is about mitigating risk in MDPs without sacrificing expected performance: arxiv.org/abs/2110.12746 Work with Paul Duckworth, Bruno Lacerda, and Nick Hawes

Without planning it, we were all in the same place at the same time today. Here's a picture of GOALS in 2022!

How do you collect valuable data from sensors far under the ocean surface? Our #IROS2022 paper's planner reasons over uncertain localisation 📍, navigation 🧭 and communication 📶 for underwater autonomous vehicles. 1/9 bit.ly/3C3ufQi Goal-Oriented Autonomous Long-Lived Systems Nick Hawes Paul Duckworth

You can find me at sigmoid.social/@pduckworth also

Friday was my final day at Oxford Robotics Institute and University of Oxford. I've had a blast. Many thanks to Nick Hawes and M A Osborne for being sensational humans and PIs. On to the next chapter.

If you're at Conference on Robot Learning #CoRL2022 and interested in planning under uncertainty for robot missions, make sure to visit Matt Budd's poster on Saturday morning! His work uses GPs as part of a Bayes-optimal framework for planning single missions in partially-known environments.

I'm excited to announce that towards the end of 2022, I started a new role InstaDeep as a Research Scientist. Since then, we've been acquired by BioNTech SE. Exciting times ahead!

We released a new version of Jumanji (v0.3) with now 18 environments, all with networks and trained checkpoints 🚀 Check out (on arXiv) Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. Paper: arxiv.org/abs/2306.09884 GitHub: github.com/instadeepai/ju…

It’s official! 🎉Today InstaDeep becomes a proud member of the BioNTech family. Want to apply your AI skills to make a difference? Join us, we’re hiring!

Fantastic achievement by Clem Bonnet and the whole Jumanji team!

Read latest #openaccess paper: Planning under uncertainty for #saferobotexploration using #Gaussianprocess prediction by A. ✨ Matt Budd Paul Duckworth Maurice Fallon Nick Hawes 🔓bit.ly/3MAWdXv Oxford Robotics Institute Oxford Dynamic Robot Systems

Excited to be sharing our work on scalable model-based search at NeurIPS tomorrow. Come chat to us! Work led by Matthew Macfarlane, Edan Toledo, Donal Byrne and Alex Laterre

Thrilled to see our NeurIPS 2024 paper, Sequential Monte Carlo Policy Optimisation (arxiv.org/abs/2402.07963), featured in Kevin's Reinforcement Learning: A Comprehensive Overview, which additionally recognises SMC as a competitive, scalable online planner. A fantastic modern