Researchers at Johns Hopkins University are studying spider crickets' jumps, hoping to one day use their design principles to create small robots to maneuver across rough terrain like earthquake rubble.
Spider crickets — members of the Orthoptera order that originated in Asia — are good models in that they jump between earthbound and airborne states.
"Let's say you're trying to search for people after a natural disaster, and that terrain is very complex, full of nooks, holes and crannies,".
"A crawling robot could only do so much. If you had a flying robot, it would also have limitations. But if a microrobot could fly, land on any surface, and take off again, as these crickets do, it would be extremely helpful."
#robotics #research #science
The video was taped on Oct 7 2015 during MICCAI 2015 at the Deep Learning Meetup Munich: http://www.meetup.com/en/deeplearning/events/225423302/ Link to video: https://www.youtube.com/watch?v=mF5-tr7qAF4
Similar talk at the Deep Learning London Meetup of Nov 4 2015: http://www.meetup.com/Deep-Learning-London/events/225841989/ (video not quite ready yet)
Most of the slides for these talks are here: http://people.idsia.ch/~juergen/deep2015white.pdf
These also includes slides for the AGI keynote in Berlin http://agi-conf.org/2015/keynotes/, the IEEE distinguished lecture in Seattle (Microsoft Research, Amazon), the INNS BigData plenary talk in San Francisco, the keynote for the Swiss eHealth summit, two MICCAI 2015 workshops, and a recent talk for CERN (some of the above were videotaped as well).
Parts of these talks (and some of the slides) are also relevant for upcoming talks in the NYC area (Dec 4-6 and 13-16) and at NIPS workshops in Montreal:
1. Reasoning, Attention, Memory (RAM) Workshop, NIPS 2015 https://research.facebook.com/pages/764602597000662/reasoning-attention-memory-ram-nips-workshop-2015/
2. Deep Reinforcement Learning Workshop, NIPS 2015 http://rll.berkeley.edu/deeprlworkshop/
3. Applying (machine) Learning to Experimental Physics (ALEPH) Workshop, NIPS 2015 http://yandexdataschool.github.io/aleph2015/pages/keynote-speakers.html
More videos: http://people.idsia.ch/~juergen/videos.html
Also available now: Scholarpedia article on Deep Learning: http://www.scholarpedia.org/article/Deep_Learning
Finally, a recent arXiv preprint: On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. http://arxiv.org/abs/1511.09249
We'll be using the second edition, currently this may be found online at http://www.cs.cmu.edu/~rwh/plbook/2nded.pdf (warning, large pdf).
As a rough content summary, these chapters lay the groundwork for the rest of the book by establishing all the math we'll be using. Because these chapters are somewhat dry, it may be interesting for us to discuss actually implementing some of the things discussed in $(your favorite PL).
Joseph Abrahamson, Craig Stuntz, and Danny Gratzer will handle leading that discussion.
- StarshipGame Developer, 2013 - presentUnity3D game development, shader programming
- Setgo LtdSoftware Developer, 2012 - 2012Client SDKs on various platforms for the company's Game Analytics service
- RacelogicJunior iOS Developer, 2011 - 2012iOS apps with MFi interfacing
- MeFreelance iOS Developer, 2010 - 2011
- Milkytea LtdMobile Game Developer, 2013 - 2013Unity3D mobile game development
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