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

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"

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"

1"Cost"of"crea7ng"hardware"and"soXware"is"decreasing"

2"Time"to"ideate,"create,"test"and"redesign"is"decreasing"

Moore’s"Law"Like"Trends"

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!!

6"

Aerial Robotics!

DI2016.pptx"

We are here!!

Aerial"Robots"

7"

We are here!!

Safe%

Speed%Smart%

Swarms%

Small%

Aerial"Robots"

8"

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"

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.

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"

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"

AI,"Autonomy"and"Society"

13"

Social"Challenges"

Democratization of S&T (opportunity and threat)"

Democra7za7on"of"Technology"

Social"Challenges"

Democratization of S&T (opportunity and threat)"

Jobs"

"

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"

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"

Social"Challenges"

Democratization of S&T (opportunity and threat)"

Jobs"

Asymmetric threats (easy to write “bad” programs)"

Safety"

Cyber physical systems security "

Privacy"

"

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"