All Posts in Category: Blog Post

ORI Volunteer at St Paul’s Girls’ School as Robotics Mentor

Over the past few months, one of our 2nd year AIMS/DPhil students, Mark Finean, has been volunteering as a robotics mentor to a group of girls pursuing their A levels at St Paul’s Girls’ School (SPGS) in London. For a school project, they wished to learn more about robots and investigate how they could be used in schools as well as the ways that students could interact with and use them to learn. To help learn more about the current state of modern robotics and gain some hands-on-experience, they spent a day in the ORI where they were  introduced to some of the research we conduct as well as tried their hands at programming the Toyota Human Support Robot (HSR). They were given a series of mini-challenges, teaching them how to programme the HSR to perform movements and tasks. This culminated in them getting the robot to approach and pick up a bottle followed by returning it to the operator. The students did remarkably well and were tremendously excited about their achievements after the day had finished. They found the day extremely useful is guiding their own ambitions for their school project where they demonstrated how a Pepper robot might be used within schools. We have since had feedback that one of the girls is now looking to pursue a career in robotics.

To further invoke interest in young students, Mark travelled to London in March along with fellow DPhil students Hala Lamdouar and Luisa Zintgraf to give presentations on Robotics and AI to SPGS and their partner schools. The talks explored the reasons why we don’t yet have the household robots that we see in science fiction and the current research going on in AI and Robotics. The talks were a resounding success and it was very rewarding to see so many young faces enthusiastic and excited about our field and the future. Being able to see inspiring female role models in particular was a hit with the students and praised for days after.

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Oxford Women in Computer Science – OxWoCS Conference

OxWoCS – 2018

The Women in Computer Science 5th annual Oxbridge conference took place on the 16th March in Cambridge.

The Oxbridge Women in Computer Science Conference is an annual conference co-organised by OxWoCS and Cambridge women@CL that aims to bring together junior and senior female computer scientists at Oxford and Cambridge, with the aim to encourage collaboration through formal and informal discussion.

This year two AIMS CDT students took part in the poster sesssion, as well as the minute madness oral presentation.  Congratulations to both Hala Lamdouar and Chia-Man Hung for taking part, and a huge congratluations goes to Hala Lamdouar for winning the best minute madenss award.




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As part of Industry week, we had the opportunity to visit Nvidia in Reading.  We had a great day, and learnt about AI at the Edge, NVIDIA’s AI Platform, JADE & Tesla, and had demonstrations on Jetson inferenecing & TensorRT.

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Paper addressing multi-agent AI problems wins top accolade

Paper addressing multi-agent AI problems wins top accolade

The winning paper ‘Counterfactual Multi-Agent Policy Gradients’ (COMA) presents a method which could soon make it possible to deploy learning multi-agent systems in the real world.

COMA differs from a lot artificial intelligence research by focussing on multi-agent problems, rather than single agent setting and two player games. There are many challenging multi-agent problems to tackle, ranging from self-driving cars to drones and even social interactions. In many of these applications a number of independent entities needs to be able to take independent actions based on local observations in order to achieve a common goal.

For example, in a fleet of search-and-rescue drones each single drone typically needs to be able to decide on its best course of action using only local information. This is commonly referred to as ‘decentralised execution’. However, often the design of the policies can be carried out in a centralised fashion, for example when training of the policies is carried out using a simulator which has access to the observations and actions of all agents. The research team believes that this domain of centralised training and decentralised execution is one of the key avenues for successfully developing and deploying multi-agent systems in the real world.

One of the great challenges when training multi-agent policies is the credit assignment problem. Just like in a football team, the reward achieved depends on the actions of all of the different agents. Given that all agents are constantly improving their policies, it is difficult for any given agent to evaluate the impact of their individual action on the overall performance of the team. To address this issue, the research team (Computer Science’s Jakob N. Foerster, Gregory Farquhar (CDT in AIMS) and Professor Shimon Whiteson, with Engineering Science’s Triantafyllos Afouras ( CDT in AIMS) and Nantas Nardelli) developed the COMA method. In the paper, the researchers model the problem setting of StarCraft unit-management as a challenging cooperative multi-agent problem. The team’s training method outperforms existing methods and achieves high win rates against the StarCraft bot.

The team’s certificate will be presented at AAAI-18 on 6 February.

Article originally published on the Department of Computer Science website: on 17 January 2018.

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Tim Seabrook and Adam Cobb will be taking up internships this year with NASA’s Frontier Development Lab (FDL).

NASA’s Frontier Development Lab (FDL) is an applied planetary science and machine learning research accelerator being run for its 2nd year, this Summer in Silicon Valley. combines machine learning experts and planetary scientists to address knowledge gaps in NASA’s Space programme.

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Mobile Robotics Week

Oxford’s AIMS (Autonomous Intelligent Machines and Systems) CDT students joined MRG to take part in a robotics challenge, working with 2nd and 3rd year DPhil students to programme a self-driving robot.

In the final challenge each robot had to work its way around obstacles to reach a goal.


winning team

Winning Team

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