human, machines, and the future of work - cra€¦ · human, machines, and ! the future of work!...
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Human, Machines, and !the Future of Work!
Vijay Kumar"Nemirovsky Family Dean of Penn Engineering"
Departments of Mechanical Engineering and Applied Mechanics, Computer and Information Science and Electrical
and Systems Engineering"
1
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3"Drivers"for"Change"
2"Cost"of"X"is"Decreasing"• Compu7ng:"$/M"transistors"<"33%/year"• Storage:"$/GB"–"38%/year"• Bandwidth:"$/Gb/s"–"27%/year"• Solar"power:"cents/kW.hr"–"75%"in"5"years"• Magnesium"• Poly"Vinyl"Chloride"• Fabrica7on"…"
2"
1"Advances"in"compu7ng,"storage,"communica7on"
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3."Speed""
Dan"Huh,"Bioengineering,"UPenn"
MakerBot"
80"cm3"hr<1""""
10"cm3"hr<1"""
40"cm3"hr<1"""2018%
2023%%%%
2013%
3<D"Prin7ng"
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1"Cost"of"crea7ng"hardware"and"soXware"is"decreasing"
2"Time"to"ideate,"create,"test"and"redesign"is"decreasing"
Moore’s"Law"Like"Trends"
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The"Game"of"Technology"
More players…"
More shots on goal…"
More scores…"
Higher scoring game …"
Attract better talent …"
5"
The rate of technology innovation is accelerating!!
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6"
Aerial Robotics!
DI2016.pptx"
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We are here!!
Aerial"Robots"
7"
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We are here!!
Safe%
Speed%Smart%
Swarms%
Small%
Aerial"Robots"
8"
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Model% Range% Resolu2on% Weight*% Power% Cost%
HDL?64E%
120"m."26.8o"ver7cal"
FOV"
<"2"cm."0.08o"(azimuth)"0.4o"(eleva7on)"
13.2"kg." 60"W" $75K"
HDL?32E%
"100"m."
41o"ver7cal"FOV""
±2"cm."0.1o"<"0.4o"(azimuth)"1.33o"(eleva7on)"
1"kg." 12W" $30K"
VLP?16%(Puck)%
100"m."30o"ver7cal"
FOV"
±3"cm."0.1o"<"0.4o"(azimuth)"
2o"(eleva7on)"830"g." 8"W" $7999"
VLP?16%Lite%%
100"m."30o"ver7cal"
FOV"
±3"cm."0.1o"<"0.4o"(azimuth)"
2o"(eleva7on)"590"g." 8"W" $9399"
Light"weight"sensing"
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Light"weight"compu7ng"
10"
Table 1: Light weight versus low power computers for autonomous flight.
ComputerIntel NUC(i3-5010U)
Intel NUC(i5-5250U)
Intel NUC(i7-5557U)
Odroid XU3(Exynos 5422)
Qualcomm Eagle(Snapdragon 801)
Cores 2 2 2 4 4
Clock Speed (GHz) 1.7 2.1 3.1 2 2.5
MFLOPS (Single-Core) 1900 2730 3440 1030 1200
MFLOPS (Multi-Core) 4250 5400 7480 4270 4350
Mass (g) 210 210 210 38 27
MFlops / g (Single-Core) 9.05 13 16.38 27.11 44.44
MFlops / g Multi-Core) 20.24 25.71 35.62 112.37 161.11
[3] Darryll J Pines and Felipe Bohorquez. Challenges facingfuture micro-air-vehicle development. Journal of air-craft, 43(2):290–305, 2006.
[4] Ascending Technologies GmbH.http://www.asctec.de/.
[5] Sergei Lupashin, Angela Schollig, Michael Sherback,and Ra↵aello D’Andrea. A simple learning strategy forhigh-speed quadrocopter multi-flips. In Robotics andAutomation (ICRA), 2010 IEEE International Confer-ence on, pages 1642–1648. IEEE, 2010.
[6] D. Mellinger and V. Kumar. Minimum snap trajectorygeneration and control for quadrotors. Robotics and Au-tomation (ICRA), 2011 IEEE International Conferenceon, pages 2520–2525, May 2011.
[7] N. Michael, S. Shen, K. Mohta, Y. Mulgaonkar,V. Kumar, K. Nagatani, Y. Okada, S. Kiribayashi,K. Otake, K. Yoshida, K. Ohno, E. Takeuchi, andS. Tadokoro. Collaborative mapping of an earthquake-damaged building via ground and aerial robots. JournalField Robotics, 29(5):832–841, 2012.
[8] Q. Lindsey, D. Mellinger, and V. Kumar. Constructionof cubic structures with quadrotor teams. Robotics Sci-ence and Systems, 2011.
[9] Matthew Turpin, Kartik Mohta, Nathan Michael, andVijay Kumar. Goal assignment and trajectory planningfor large teams of aerial robots. Robotics: Science andSystems (RSS), June 2013.
[10] A. Bachrach, A. de Winter, R. He, G. Hemann, S. Pren-tice, and N. Roy. Range - robust autonomous navigationin gps-denied environments. In Robotics and Automa-tion (ICRA), 2010 IEEE International Conference on,pages 1096–1097, May 2010.
[11] Dimitrios G Kottas, Joel A Hesch, Sean L Bowman, andStergios I Roumeliotis. On the consistency of vision-aided inertial navigation. In Experimental Robotics,pages 303–317. Springer, 2013.
[12] Friedrich Fraundorfer, Lionel Heng, Dominik Honegger,Gim Hee Lee, Lorenz Meier, Petri Tanskanen, and MarcPollefeys. Vision-based autonomous mapping and explo-ration using a quadrotor mav. In Intelligent Robots and
Systems (IROS), 2012 IEEE/RSJ International Con-ference on, pages 4557–4564. IEEE, 2012.
[13] Albert S Huang, Abraham Bachrach, Peter Henry,Michael Krainin, Daniel Maturana, Dieter Fox, andNicholas Roy. Visual odometry and mapping for au-tonomous flight using an rgb-d camera. In Interna-tional Symposium on Robotics Research (ISRR), vol-ume 2, 2011.
[14] Yash Mulgaonkar, Michael Whitzer, Brian Morgan,Christopher M Kroninger, Aaron M Harrington, and Vi-jay Kumar. Power and weight considerations in small,agile quadrotors. In SPIE Defense+ Security, pages90831Q–90831Q. International Society for Optics andPhotonics, 2014.
[15] Vijay Kumar and Nathan Michael. Opportunitiesand challenges with autonomous micro aerial vehi-cles. The International Journal of Robotics Research,31(11):1279–1291, 2012.
[16] G. Loianno, Y. Mulgaonkar, C. Brunner, D. Ahuja,A. Ramanandan, M. Chari, S. Diaz, and V. Kumar.Smartphones power flying robots. In Intelligent Robotsand Systems (IROS), 2015 IEEE/RSJ InternationalConference on, pages 1256–1263, Sept 2015.
[17] Christian Forster, Matia Pizzoli, and Davide Scara-muzza. SVO: Fast semi-direct monocular visual odome-try. In IEEE International Conference on Robotics andAutomation (ICRA), 2014.
[18] S. Shen, Y. Mulgaonkar, N. Michael, and V. Kumar.Multi-sensor fusion for robust autonomous flight in in-door and outdoor environments with a rotorcraft mav.In 2014 IEEE International Conference on Robotics andAutomation (ICRA), pages 4974–4981, May 2014.
[19] T. Lee, M. Leoky, and N. H. McClamroch. Geomet-ric tracking control of a quadrotor uav on se(3). In49th IEEE Conference on Decision and Control (CDC),pages 5420–5425, Dec 2010.
[20] DJI. http://www.dji.com/.
[21] Pixhawk Autopilot. https://pixhawk.org.
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Autonomy"in"Cyber"Physical"Systems"
Recent"advances""• Systems"can"learn"to"be"autonomous"– Data"driven"approaches"to"percep7on,"ac7on"
• Systems"can"share"data,"learn"from"each"other"
"11"
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AI:"6"myths/challenges"
• The"ability"to"crunch"through"large"amounts"of"data"(which"we"some7mes"mistakenly"call"learning)"does"not"translate"to"knowledge""
• The"ability"to"make"complex"calcula7ons"rapidly"does"not"translate"to"autonomy"
• 99.99%"correct"is"exponen7ally"harder"to"achieve"than"90%"correct"
• Percep7on"+"ac7on"is"exponen7ally"harder"than"just"percep7on"
• Tasks"with"physical"contact"is"exponen7ally"harder"than"tasks"like"driving"or"flying"
• Human"machine"collabora7on"is"impossible"unless"they"share"representa7ons."We"only"know"how"to"do"this"at"some"very"simple"levels."
12"
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AI,"Autonomy"and"Society"
13"
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Social"Challenges"
Democratization of S&T (opportunity and threat)"
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Democra7za7on"of"Technology"
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Social"Challenges"
Democratization of S&T (opportunity and threat)"
Jobs"
"
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Social"Disrup7on"
17"
Type of Job! Qualification! Wages!
Highly skilled, analytical, making decisions"
Advanced degree, tech skills balanced with emotional judgment"
$$$$"
Analytical, drawing inferences"
College degree" $$$"
Modest skills, some labor"
High school education"
$$"
Unskilled labor" reading/writing" $"
2016"
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Social"Disrup7on"
18"
Type of Job! Qualification! Wages!
Highly skilled, analytical, making decisions"
Advanced degree, tech skills balanced with emotional judgment"
$$$$"
Analytical, drawing inferences"
College degree" $$$"
Modest skills, some labor"
High school education"
$$"
Unskilled labor" Manual skills" $"
2020"
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Social"Challenges"
Democratization of S&T (opportunity and threat)"
Jobs"
Asymmetric threats (easy to write “bad” programs)"
Safety"
Cyber physical systems security "
Privacy"
"
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Summary"
Autonomy in Cyber Physical systems (as we imagine) will be difficult to achieve"
Time scale of technology change (years) is much shorter than time scale of a human (decades)"
Our role"
- Safety"
- Security"
"
- Privacy"
- Education/training"