1. Stress Relief Video

2. Team WPI-CMU and the DARPA Robotics Challenge Chris Atkeson July 9, 2015

3. DARPA Robotics Challenge

4. Day 1

5. 3 Stages Simulation: VRC; Team Steel -> WPI-CMU Isolated tasks: DRC Trials, Dec. 2013 –8 tasks, 30 minutes/task. –Safety belays. –External power. –WPI-CMU: Fastest driving! Sequence of tasks: DRC Finals, Jun. 2015 8 tasks, 1 hour No safety belays or external power Simplified Trials tasks: –no hose, ladder->stairs, surprise task

6. Team Steel: VRC Was A Disaster I can’t manage my way out of a paper bag. VRC

7. The Crack of Doom Chris Atkeson VRC

8. Add Safety Features To Handle User Error An exhausted and distracted user (me) crashed the robot twice by typing in the wrong command (for example, 0.23 instead of 2.3 for yaw angle). VRC

9. Make Sure Your Safety Features Don’t Kill You Suicide Bug: A safety feature was added late in the game so if the robot fell, it fell in a way that was easier to recover. Unfortunately, this feature triggered on false alarms several times in the VRC, causing the robot to fall when nothing was wrong. We have been unable to reproduce the orientation measurement glitches that caused the false alarms on our own computers. Only testing on VRC computers would have detected it. VRC

10. What we should have done Start with fully teleoperated systems, and then gradually automate and worry about bandwidth limitations. Formal code releases Better interfaces Periodic group activities that simulated tests or did other things that got people to integrate and test entire systems. VRC

11. Project Management Rules We Violated Freeze early and test, test, test. –Detect crack of doom bug, –Don’t introduce suicide bug –Resist temptation to tweak Put in safety features to be robust to tired distracted human users. Make sure your safety features don’t kill you. Suicide bug was not robust to false alarms. Don’t have project leader also run a division: lose an overall firefighter and skeptic. VRC

12. Issues That Required Effort State estimation, particularly with point or edge contact in rough terrain (wobbly foot). Driver dynamics. Keep planner from doing stupid things. We found designing robust behaviors very time consuming. We need better tools. Integration “API”. How do we specify behavior interconnections? VRC

13. DRC Trials: Schaft video Trials

14. Terrain Trials

15. DARPA Robotics Challenge Trials WPI-CMU Challenges –Modeling error –Full-body behavior –Affordability (make cheaper robot) Accomplishments –Whole body control –Best ladder climbing of Atlas robots –Fastest driving Trials

16. DRC Trials: Failures Operator error on Drill -> Idiot Proof Interfaces: Minimize interface: no typing, no click boxes or other options, no pop-up menus. … Wind on Doors -> Practice in a hurricane or wind tunnel (which we did). Knee torque limit on Terrain (and maybe belay) -> Explore and know robot limits. Trials

17. 3 Classes of Robots Atlas Robots Bipeds: SCHAFT, all others Non-bipeds: CHIMP, RoboSimian Trials

18. Speed All robots were VERY slow –Perception? –Planning? –Rational? A lot of the time the robot was not moving Trials

19. Walk and Push Wind on doors: needed to walk and apply force or hold position at same time. Trials

20. Bump and Go No one could get out of the car. Presumably, no one could get into it either. “Bump, Lean, and Go” locomotion Trials

21. Ladder Few teams seriously attempted the ladder The ladder was really steep stairs. If your kinematics matched it, it was a trivial task. If your kinematics did not match it, it was a whole body locomotion task. Trials

22. Ladder Trials

23. Kinematic Targets Both rough terrain and the ladder, locomotion were dominated by tight kinematic targets. Basically these are all stepping stone problems. This is different from most research on legged locomotion. Trials

24. DRC Trials … Debris was hard for a lot of teams –Planning? –Perception? –Execution? Screwing in the hose was hard as well. Trials

25. 2013 Atlas Similar to Sarcos Humanoid Force sensing done with oil pressure Joint angle sensing done at actuator 3 Behaviors: –Step: some groups used this extensively –Walk: fast, not used –Manipulate: user controls upper body Trials

26. 2013 Atlas Issues Kinematic Error – 7cm at foot Stiction – 20Nm Knee too weak Torso (particularly pitch) was not strong enough, lots of kinematic error. Couldn’t see feet (knees in way). Hard to see hands (limited neck, occlusion) Trials

27. CMU Behaviors Walk Manipulate Trajectory and then QP optimization: See Whitman thesis, Feng Humanoids 2013 paper. QP-based inverse kinematics as well as inverse dynamics: allow joint servos as well as velocity constraints. Trials

28. Secrets of our ladder, walking Use visual feedback to human operator to guide hand and foot placement. Use estimated foot location if you can’t actually see your foot. Draw foot on video. “Nudging” user interface. Trials

29. Challenges for Final More robust walking: No safety ropes: Never fall down Robust CMU-manipulate Get in and out of car Use railings on ladder: weak arms and hands Doors: walk while applying force Debris: pre-plan Trials

30. Optimization All The Way Down Multi-level optimization: –Optimization-Based Inverse Dynamics: Greedy optimization (QP) for full body at the current instant. –Trajectory Optimization (Continuous across time) –Footstep Optimization (Discrete + continuous across time) Trials

31. Two Level Control Level 1: External forces at contacts drive center of mass (COM). Rotation is more complicated: –Constant angular momentum –Rigid body equivalent –General case Level 2: Redundancies and constraints resolved for full body behavior. Trials

32. Level 1: Thinking About The Future Use simple models. Can we just think about COM, or does angular momentum matter? LIPM Trials

33. LIPM Trajectory Optimization X vs, Time Y vs. Time COM Footstep COM X vs. Y meters Trials

34. Level 2: Optimization-Based “Inverse Dynamics” (QP) Objectives: Dynamics Task Objectives COM Acceleration Torque About COM Reference Pose Tracking Minimize Controls Constraints: Center of Pressure Friction Cone Joint Torque Limits M. de Lasa, I. Mordatch, and A. Hertzmann, “Feature-Based Locomotion Controllers,” ACM Transactions on Graphics, vol. 29, 2010. Stephens Trials

35. Optimization? + Can help you solve complex problems with many factors. + Skills can be combined during optimization/practice/learning. - Often hard to choose constraints and weights to get what you really “want”. - If your tools are slow (need to do a simulation to check things out) this design process is slow. - Not reliable: similar inputs may lead to very different results. Use more hard constraints? Prioritized vs. single-level optimization Trials

36. 1.5 years

37. Watch the DRC Finals! Finals

38. Team WPI-CMU Did well (14/16 points over 2 days, drill) Did not fall Did not require physical human intervention Tried all tasks (eliminates RoboSimian, which skipped stairs). Safety code worked. Operator interface and operators worked. State estimation, walking, and manipulation core code worked very well. Finals

39. Slow and Steady vs. Fast and Flaky We knew we were going to be slow –Reliable walk –How we used human operators –Lack of total autonomy plus communications delay. Strategy: Assume other teams will rush and screw up (which happened). Assume Atlas repairs will not be possible. Finals

40. Day 2 Finals

41. Real Time Finals

42. Walking Manipulation Finals

43. Handling modeling error and external forces Finals

44. Stuck on the door Finals

46. Failed manipulation Finals

47. Fall Predictors Finals

48. A bad step Finals

49. Egress: Maximize Contact Finals

50. Discrete/Continuous Optimization Offline + Online Optimization Finals

51. 2015 Atlas New electric forearms add a 7 th arm degree of freedom. Huge difference. Weak, flaky, poorly sensed electric forearms, like T. Rex. Lower body, torso problems all fixed. Limited foot sensing Fz, Mx, My but not Fx, Fy, Mz. Finals

52. WPI-CMU vs. IHMC, MIT walks We were slower (8s stride) but took longer footsteps (0.4m). Our max speed is 1.6s stride, 0.4m/s We do gentle steps, IHMC/MIT have shock waves. We use foot sensor Fz, COP, they only use it as a binary contact sensor. Was anybody compliant? See fall videos. Finals

53. IEEE Spectrum video Finals

54. Perception Surprisingly good kinematic odometry and state estimation. Multisense: Stereo vision and rotating Hokuyo Wrist and knee cameras for operators. Relative measurements, forget past, no world model (Rod Brooks comes back to haunt me). Users mark pixels with lines, scribbles. System automatically segments indicated object. Finals

55. What we learned from our work IK is still a problem. Blend, don’t switch There is always something broken. Learning to plan with a planner hierarchy is hard. Finals

56. Wheels win? All wheeled/tracked vehicles plowed through debris. All other vehicles walked over rough terrain. KAIST – walked on stairs Nimbro, RoboSimian – no stairs Leg/wheel hybrids good if there is a flat floor somewhere under the pile of debris. Wheeled/tracked vehicles fell: need to consider dynamics, need to be able to get up (CHIMP, NimbRo), and get un-stuck. Finals

57. Whole-Body Locomotion NO ROBOT used railings, walls, door frames, or objects in the environment for physical guidance, stabilization, or support. WOW. EVEN DRUNK PEOPLE ARE SMARTER THAN THAT!!!!!!!!!!!!!!!!!!!!!!!!! Finals

58. Operator Errors Dominated IHMC, CHIMP, MIT, WPI-CMU … HRI Matters Finals

59. Most Teams Had A Major Bug Slip Through Testing. Our bug was an incorrect Finite State Machine for the Drill Task, which led to the drill being dropped. The 2 nd day attempt at the drill task failed because the right forearm overheated and shut off. We had a two handed strategy (bad). We had evidence that this could happen, but failed to act on it. Finals

60. Trials Finals 8 KAIST 8 IHMC 8 CHIMP 7 NimbRo 7 RoboSimian 7 MIT 7 WPI-CMU 6 DRC-HUBO UNLV 5 TRACLabs 5 AIST-NEDO 4 NEDO-JSK 27 Schaft 20 IHMC 18 CHIMP 16 MIT 14 RoboSimian 11 TRACLabs 11 WPI-CMU 9 Trooper 8 Thor 8 Vigir 8 KAIST 3 HKU 3 DRC-HUBO-UNLV

61. My Awards Most Improved Robot: DRC-Hubo Luckiest Team: IHMC Unluckiest Teams: CHIMP, MIT Most Cost Effective Robot: Momaru (NimbRo) Most Aesthetically Pleasing Egress: RoboSimian Slow But Steady Award: WPI-CMU

62. Upcoming Thesis Defences July 27?: State estimation, fall detection, prevention, handling. Xinjilefu Jan: Walking and Manipulation core controller. Siyuan Feng. Current

63. Current Work Current

64. Design Robots So Falling OK Current

66. Are Challenges a good idea? Does doing the challenge crowd out research? It certainly caused us to put some research on hold, but also led to new issues and redirected our research to some extent. Does the challenge make us more productive? In the short term, yes. In the long term? Conflict between developing conservative and reliable deployable systems, and understanding hard issues like agility.