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
VRC
Team Steel: VRC Was A Disaster
• I can’t manage my way out of a paper bag.
VRC
The Crack of Doom
Chris 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).
Make Sure Your Safety Features
Don’t Kill You
VRC
• 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.
What we should have done
VRC
• 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.
Project Management Rules We
Violated
VRC
• 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.
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 video
Trials
Trials
Terrain
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
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
Ladder
Trials
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
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•
•
•
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)
Two Level Control
Trials
• Level 1: External forces at contacts drive
center of mass (COM).


F

m
x

Rotation is more complicated:

(  r F )  0
– Constant angular momentum
– Rigid body equivalent

(


r

F
)

I

   I
– General case


(


r

F
)

L
 d ( I) / dt

• Level 2: Redundancies and constraints
resolved for full body behavior.
Trials
Level 1: Thinking About The Future
• Use simple models. Can we just think about COM,
or does angular momentum matter?
• LIPM
x  [( x  p ) g  a ] / h
LIPM Trajectory Optimization
X vs, Time
COM
Footstep
X vs. Y
COM
Footstep
Y vs. Time
COM
meters
Footstep
Trials
Trials
Level 2: Optimization-Based
“Inverse Dynamics” (QP)
Objectives:
• Dynamics
• Task Objectives
• COM Acceleration
• Torque About COM
• Reference Pose Tracking
• Minimize Controls
 w1 A1 
 w1b1 
 w A  q  w b 
 2 2    2 2 
 w3 A3      w3b3 


  
       
w b 
 wN AN 
 N N
Constraints:
• Center of Pressure
• Friction Cone
• Joint Torque Limits
 q 
 
C     d 
 
 
Stephens
M. de Lasa, I. Mordatch, and A. Hertzmann, “Feature-Based Locomotion Controllers,” ACM Transactions on Graphics, vol. 29, 2010.
Optimization?
Trials
• + 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
1.5 years
Finals
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
Real Time
Finals
Finals
Walking
Manipulation
Finals
Handling modeling error
and external forces
Finals
Stuck on the door
Finals
Finals
Failed manipulation
Finals
Fall Predictors
Finals
A bad step
Finals
Egress: Maximize Contact
Finals
Discrete/Continuous Optimization
Offline + 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
Perception
Finals
• 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
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•
•
•
IK is still a problem.
Blend, don’t switch
There is always something broken.
Learning to plan with a planner hierarchy
is hard.
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
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.
Trials
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•
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•
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•
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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
Finals
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•
•
•
•
•
•
•
•
•
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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
My Awards
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•
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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
Current
Upcoming Thesis Defences
• July 27?: State estimation, fall detection,
prevention, handling. Xinjilefu
• Jan: Walking and Manipulation core
controller. Siyuan Feng.
Current
Current Work
Design Robots So Falling OK
Current
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.