Transcript Atkeson talk

DARPA Robotics Challenge
www.cs.cmu.edu/~cga/dw
Day 1
Real Time
Finals
Trials
Optimization All The Way Down
• Multi-level optimization:
– Footstep Optimization (Discrete + Continuous)
– Trajectory Optimization (Continuous)
– Optimization-Based Inverse Dynamics: Greedy
continuous optimization (Quadratic Program =
QP) for full body at the current instant.
Multi-Level Control
Trials
• Trajectory: 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

• QP: Redundancies and constraints resolved
for full body behavior.
LIPM Trajectory Optimization
X vs, Time
COM
Footstep
X vs. Y
COM
Footstep
Y vs. Time
COM
meters
Footstep
Trials
Trials
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.
Finals
Stuck on the door
Finals
Handling modeling error
and external forces
Finals
Finals
Failed manipulation
Finals
Fall Predictors
Finals
A bad step
Why Dynamics Matters
Wheels win?
Finals
• All wheeled/tracked robots plowed through
debris.
• All other robots 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.
• Some robots used arms to get out of the
car.
Can Atlas Be Human Like?
www.cs.cmu.edu/~cga/dw
• Slow (0.5m/s)
• Slow (0.6s/step)
• Short steps 0.35m
(half human 0.7m)
• Flat foot walking
• Bent knees
• No heel strike or
toe push off
• Fixed pelvis
orientation
• Minimal vertical COM
acceleration -> GRF
• Minimal horizontal
COM acc. -> GRF
• COP fixed at center of
foot
• Not robust to external
perturbations or
modeling error.
• Soft ground, sand,…?
Current
Baby Steps To Human-Like Walking
Current