stress relief video. team wpi-cmu and the darpa robotics challenge chris atkeson july 9, 2015

Stress Relief Video

Team WPI-CMU and the DARPA Robotics Challenge

Chris Atkeson

July 9, 2015

DARPA Robotics Challenge

Day 1

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

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

VRC

The Crack of DoomChris Atkeson

VRC

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

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

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

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

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

DRC Trials: Schaft videoTrials

TerrainTrials

DARPA Robotics Challenge TrialsWPI-CMU

• Challenges

–Modeling error

–Full-body behavior

–Affordability (make cheaper robot)

• Accomplishments

–Whole body control

–Best ladder climbing of Atlas robots

–Fastest driving

Trials

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

3 Classes of Robots

• Atlas Robots

• Bipeds: SCHAFT, all others

• Non-bipeds: CHIMP, RoboSimian

Trials

Speed

• All robots were VERY slow– Perception?– Planning?– Rational?

• A lot of the time the robot was not moving

Trials

Walk and Push

• Wind on doors: needed to walk and apply force or hold position at same time.

Trials

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

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

LadderTrials

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

DRC Trials …

• Debris was hard for a lot of teams– Planning?– Perception?– Execution?

• Screwing in the hose was hard as well.

Trials

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

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

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

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

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.

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IIFr )(

0)( Fr

dtIdLFr /)()(

Trials

Level 1: Thinking About The Future

• Use simple models. Can we just think about COM, or does angular momentum matter?

• LIPM

hagpxx /])[(

Trials

LIPM Trajectory Optimization

X vs, Time

Y vs. Time

COM

Footstep

Footstep

Footstep

COM

COM

X vs. Y

meters

Trials

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

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Aw

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33

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11

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M. de Lasa, I. Mordatch, and A. Hertzmann, “Feature-Based Locomotion Controllers,” ACM Transactions on Graphics, vol. 29, 2010.

Stephens

Trials

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

1.5 years

Watch the DRC Finals!

Finals

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

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

Day 2

Finals

Real TimeFinals

Walking Manipulation

Finals

Handling modeling error and external forces

Finals

Stuck on the door

Finals

Finals

Failed manipulation

Finals

Fall Predictors

Finals

A bad stepFinals

Egress: Maximize ContactFinals

Discrete/Continuous OptimizationOffline + Online Optimization

Finals

2015 Atlas

• New electric forearms add a 7th 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

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

IEEE Spectrum video

Finals

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

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

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

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

Operator Errors Dominated

• IHMC, CHIMP, MIT, WPI-CMU …

• HRI Matters

Finals

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 2nd 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

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

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

Upcoming Thesis Defences

• July 27?: State estimation, fall detection, prevention, handling. Xinjilefu

• Jan: Walking and Manipulation core controller. Siyuan Feng.

Current

Current Work

Current

Design Robots So Falling OKCurrent

Current

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.