1. Robotic Space Explorers Brian C. Williams Space Systems Lab & Artificial Intelligence Lab, MIT

2. Marskokhod at NASA Ames research center

3. Smart Buildings at CMU & Xerox PARC

4. Ecological Life Support For Mars Exploration

5. Courtesy of Yuri Gawdiak, NASA Ames

6. courtesy Dave Miller, SSL MIT

7. Robotic Webs

8. Unmanned Air Vehicles

9. Intelligence Embedded at all Levels Behavior-based robotics: Subsumption Reinforcement learning and MDPS Classical planning and execution Model-based diagnosis and execution Mission-level planning Robotic path planning Probabilistic monitoring and Decision-theoretic planning Multi-agent coordination Increased Reasoning

10. To Boldly Go Where No AI System Has Gone Before A Story of Survival

11. courtesy JPL Started: January 1996 Launch: Fall 1998

12. Remote Agent Team Members Douglas BernardJPL Steve ChienJPL Greg DoraisAmes Julia DunphyJPL Dan DvorakJPL Chuck FryAmes Ed GambleJPL Erann GatJPL Othar HanssonThinkbank Jordan HayesThinkbank Bob KanefskyAmes Ron KeesingAmes James KurienAmes Bill MillarAmes Sunil MohanFormida Paul MorrisAmes Nicola MuscettolaAmes Pandurang NayakAmes Barney PellAmes Chris PlauntApple Gregg RabideauJPL Kanna RajanAmes Nicolas RouquetteJPL Scott SawyerLMMS Rob SherwoodJPL Reid SimmonsCMU Ben SmithJPL Will TaylorAmes Hans ThomasAmes Michael Wagner4th Planet Greg WhelanCMU Brian C. WilliamsAmes David YanStanford

13. I am a HAL 9000 computer production number three. I became operational at the H.A.L. plant in Urbana, Illinois on January 12, 1997.

14. International Space Station 1998-2002 courtesy NASA

15. ``Our vision in NASA is to open the Space Frontier. When people think of space, they think of rocket plumes and the space shuttle. But the future of space is in information technology. We must establish a virtual presence, in space, on planets, in aircraft and spacecraft.’’ - Daniel S. Goldin, NASA Administrator Sacramento, California, May 29, 1996 Motive: Astrobiology & Origins Programs Means: New Millennium Program Smarts: Autonomous Reasoning

16. 1997: Mars Pathfinder and Sojourner Motive: Primitive life on Early Mars? courtesy JPL

17. Mars Pathfinder, 1997 courtesy JPL New Means

18. courtesy NASA Ames courtesy NASA Lewis New Means: Mars Airplane

19. Cryobot & Hydrobot courtesy JPL Motive: life under Europa?

20. Formation Flying Optical Interferometer (ST3) courtesy JPL Motive: Earth-like Planets Around Other Stars?

21. Four launches in 7 months Mars Climate Orbiter: 12/11/98 Mars Polar Lander: 1/3/99 Stardust: 2/7/99 QuickSCAT: 6/19/98 courtesy of JPL

22. Traditional spacecraft commanding

23. How Will They Survive? Vanished: Mars Observer Mars Polar Lander Miscommanded Clementine Mars Climate Orbiter courtesy of JPL

24. STS-93 Hydrogen Leak Symptoms: Engine temp sensor high LOX level low GN&C detects low thrust H2 level low (???) Problem : Liquid hydrogen leak Effect: LH2 used to cool engine Engine runs hot Consumes more LOX

25. Cassini Maps Titan courtesy JPL 7 year cruise ~ 150 - 300 ground operators ~ 1 billion $ 7 years to build Intelligent Embedded Systems: Cassini 150 million $ 2 year build 0 ground ops Faster, Better, Cheaper

26. courtesy JPL Ames-JPL NewMaap: New Millennium Advanced Autonomy Prototype July - November, 1995 no Earth Comm ~ 1 hr insertion window engines idle for several years moves through ring plane

28. Reconfiguring for a Failed Engine Fire backup engine Valve fails stuck closed Open four valves Fuel tank Oxidizer tank

29. AI in the pre-90’s: Reservations about Embedded Systems being Intelligent “[For reactive systems] proving theorems is out of the question” [Agre & Chapman 87] ``Diagnostic reasoning from a tractable model is largely well understood. [However] we don’t know how to model complex behavior...’’ [Davis & Hamscher 88] “[Commonsense] equations are far too general for practical use.” [Sacks & Doyle 91]

30. Houston, We have a problem... courtesy of NASA Quintuple fault occurs (three shorts, tank-line and pressure jacket burst, panel flies off). Mattingly works in ground simulator to identify new sequence handling severe power limitations. Mattingly identifies novel reconfiguration, exploiting LEM batteries for power. Swaggert & Lovell work on Apollo 13 emergency rig lithium hydroxide unit.

31. Challenge: Thinking Through Interactions Programmers must reason through system-wide interactions to generate codes for: command confirmationcommand confirmation goal tracking goal tracking detecting anomalies detecting anomalies isolating faults isolating faults diagnosing causes diagnosing causes hardware reconfig hardware reconfig fault recovery fault recovery safing safing fault avoidance fault avoidance control coordination control coordination Equally problematic at mission operations level

32. Programmers generate breadth of functions from commonsense models in light of mission goals. Model-based Programming Program by specifying commonsense, compositional declarative models. Model-directed Planning & Execution Provide services that reason through each type of system interaction from models. on the fly reasoning requires significant search & deduction within the reactive control loop. Model-based Autonomy

33. Transition Systems + Constraints + Probabilities ClosedValveOpen Stuckopen Stuckclosed OpenClose 0. 01 0.01 0.01 inflow = outflow = 0 Towards a Unified Model Mission Operations ModelHardware Commanding & Failure Model

34. Fast Search: Deep Blue beats Kasparov by brute force.

35. Phase Transition for 3-SAT, N = 12 to 100 Data Rescaled Using  c = 4.17, = 1.5 (Kirkpatrick and Selman, Science, May 1994) 100 24 40 50 1 Many problems aren’t so hard

36. FOUND UNSOLVABLE SOLUTION FOUND Average constraints per variable 4-sat cost 25 var. Many problems aren’t so hard generate successor generate successor Agenda TMS conflict database conflict database RISC-like, deductive kernel General Deduction Can Achieve Reactive Time Scales

37. Quintuple fault occurs (three shorts, tank-line and pressure jacket burst, panel flies off). Mattingly works in ground simulator to identify new sequence handling severe power limitations. Mattingly identifies novel reconfiguration, exploiting LEM batteries for power. Swaggert & Lovell work on Apollo 13 emergency rig lithium hydroxide unit. Multiple fault diagnosis of unexperienced failures. Mission planning and scheduling Hardware reconfiguration Scripted execution Services For Thinking Through Interactions

38. Real-Time Execution Flight H/W Fault Monitors Planning Experts (incl. Navigation) Remote Agent Architecture Ground System RAX Manager Diagnosis & Reconfig Mission Manager Scripted Executive Planner/Scheduler Remote Agent RAX_START

39. courtesy JPL Ames-JPL NewMaap: New Millennium Advanced Autonomy Prototype July - November, 1995

40. Thrust Goals Attitude Point(a) Engine Off Delta_V(direction=b, magnitude=200) Power Mission Manager Sets Goals

41. Thrust Goals Attitude Turn(a,b) Point(a)Point(b)Turn(b,a) Engine Thrust (b, 200) Off Delta_V(direction=b, magnitude=200) Power Warm Up Plan!

42. Planner Models Objects –state-variables –tokens Constraints –compatibilities –functional dependencies

43. Compatibility Thrust Goals Attitude Engine Thrust (b, 200) Delta_V(direction=b, magnitude=200) Power contains

44. Thrust Goals Attitude Point(b) Engine Thrust (b, 200) Off Delta_V(direction=b, magnitude=200) Power Warm Up meets met_by contained_by equals Compatibility

45. Planning/Scheduling Cycle Plan has flaws Instantiate compatibility Schedule token Plan is consistent Backtrack YES Uninstantiated compatibility............ NO PLAN Heuristics

46. Types of Plan Flaws Un-instantiated compatibilities –subgoaling Un-inserted tokens Under-constrained parameter Gaps in scheduling horizon

47. Plan Generates A Simple Temporal Constraint Network       

48. DS1 Planner/Scheduler summary Model size (Remote Agent Experiment) – state variables18 – token literal types42 – compatibility specs46 Plan size –tokens154 – temporal relations180 –variables288 (81 time points) –constraints232 (114 distance bounds) Performance – search nodes 649 –search efficiency 64 %

49. Executing Temporal Plans [130,170]] [0, 300]   Propagate time Select enabled events Terminate preceding tokens Run next tokens

50. EXECUTIVE CONTROLLED SYSTEM Time Propagation Can Be Costly

51. EXECUTIVE CONTROLLED SYSTEM Compile to Efficient Network

52. Programmers and operators must reason through system-wide interactions to generate codes for: Identifying ModesReconfiguring Modes monitoring monitoring tracking goals tracking goals confirming commands confirming commands detecting anomalies detecting anomalies diagnosing faults diagnosing faults reconfiguring hardwarereconfiguring hardware coordinating control policies coordinating control policies recovering from faults recovering from faults avoiding failures avoiding failures Model-based Execution of Tokens

53. Model-based Execution as Stochastic Optimal Control Controller Plant mode identification mode reconfiguration s’(t)  (t) f f s (t) g g o(t) Model Livingstone Goals

54. Closed Open Stuck open Stuck closed Open Close Cost 5 Prob.9 Models modes engage physical processes probabilistic automata for dynamics Vlv = closed => Outflow = 0; vlv=open => Outflow = M z + (inflow); vlv=stuck open => Outflow = M z + (inflow); vlv=stuck closed=> Outflow = 0;

55. Model-based programming language (defcomponent valve (?name) :attributes ((sign-values (flow (input ?name))) (sign-values (flow (output ?name)))...)... (closed :model (and (= (flow (input ?name)) zero) (= (flow (output ?name)) zero)) :transitions ((open-valve :when (open (cmd-in ?name)) :next open) (:otherwise :persist)))...)

56. Mode Identification and Diagnosis Observe “no thrust” Find most likely reachable states consistent with observations.

57. Goal: Achieve Thrust Mode Reconfiguration

58. Models Compile to Propositional Logic Specifying Variables mode ranges over {open, closed, stuck-open, stuck-closed} cmd ranges over {open, close, no-cmd} f in, and f out range over {positive, negative, zero} p in, and p out ranging over {high, low, nominal} Specifying Mode Behaviors mode = open  (p in = p out )  (f in = f out ) mode = closed  (f in = zero)  (f out = zero) mode = stuck-open  (p in = p out )  (f in = f out ) mode = stuck-closed  (f in = zero)  (f out = zero)

59. Specifying nominal transitions mode = closed  cmd = open  next (mode = open) mode = closed  cmd ≠ open  next (mode = closed) mode = open  cmd = close  next (mode = closed) mode = open  cmd ≠ close  next (mode = open) mode = stuck-open  next (mode = stuck-open) mode = stuck-closed  next (mode = stuck-closed) Specifying failure transitions –  1 : mode = closed  next (mode = stuck-closed) –  2 : mode = closed  next (mode = stuck-open) –  3 : mode = open  next (mode = stuck-open) –  4 : mode = open  next (mode = stuck-closed)

60. Mode identification and reconfiguration performed by OPSAT generate best implicants generate best implicants Best-first Agenda Check Constraints Optimalfeasiblemodes Conflicts(infeasible modes) modes) Checkedmodes DPLL SAT With ITMS DPLL SAT With ITMS conflict database conflict database Combinatorial optimization w propositional constraints A* + Conflict-directed Search + DPLL + TMS

61. MI and MR performance Number of components: 80 Number of clauses: 11101 no TMS LTMS

62. courtesy JPL Started: January 1996 Launch: Fall 1998

63. Remote Agent Experiment May 17-18th experiment Generate plan for course correction and thrust Diagnose camera as stuck on –Power constraints violated, abort current plan and replan Perform optical navigation Perform ion propulsion thrust May 21th experiment. Diagnose faulty device and –Repair by issuing reset. Diagnose switch sensor failure. –Determine harmless, and continue plan. Diagnose thruster stuck closed and –Repair by switching to alternate method of thrusting. Back to back planning See rax.arc.nasa.gov

64. Intelligent embedded systems research has addressed each of AI’s concerns “[For reactive systems] proving theorems is out of the question” [Agre & Chapman 87] ``Diagnostic reasoning from a tractable model is largely well understood. [However] we don’t know how to model complex behavior...’’ [Davis & Hamscher 88] “[Commonsense] equations are far too general for practical use.” [Sacks & Doyle 91]

65. “With autonomy we declare that no sphere is off limits. We will send our spacecraft to search beyond the horizon, accepting that we cannot directly control them, and relying on them to tell the tale.” Bob Rasmussen Architect JPL Mission Data System