robots and humans - aude billard
TRANSCRIPT
Teaching Robots to
Drink, Relax and Play catch
Factory Robots
Factory Robots
Factory robots live in a human-less world.
Factory robots function in a world that is fully predetermined,
where there is no room for change.
Factory Robots
Going into the real world
Unlike industrial settings, the world
in which we live changes all the
time:
Cannot predict all circumstances
Need to react rapidly and
appropriately
Robots that deal with uncertainty
Commercial airplanes fly autonomously to a very large extent.
They can recover from turbulences automatically.
Automobile industry and governments support research
to build fully autonomous cars.
Robots that deal with uncertainty
No two apartments
look the same
And the same apartment
can change appearance
from one day to the next
Uncertainty in home environment
What does it mean to grate carrots?
More than one way to do this.
More than one tool to
perform the task.
Variability in task definition
Learning from Human Demonstrations
Learning a skill is more than simply replaying a trajectory.
It requires to understand what a skill is.
To learn this, one needs to show several demonstrations
to generalize across sets of examples.
Learning from Human Demonstrations
Learning a skill is more than simply replaying a trajectory.
It requires to understand what a skill is.
To learn this, one needs to show several demonstrations
to generalize across sets of examples.
Learning from Human Demonstrations
K. Kronander, M. Khansari and A. Billard, JTSC Best Paper Award, Int. Conf. on Intelligent and Robotics Systems, IROS 2011
Learning from Human Demonstrations
Teaching robot how to adapt to perturbations
http://lasa.epfl.ch
Being stiff is not always good How to teach a robot to relax…
Teaching robots to be less stiff
Low stiffness when carrying the liquid High stiffness when pouring the liquid
http://lasa.epfl.ch
Shaking the robot: A natural method to teach a robot to relax.
Teaching robots to be less stiff
Being stiff is not always good How to teach a robot to relax…
http://lasa.epfl.ch
After training the robot manages to adapt naturally when
required and remains stiff when required.
Teaching robots to be less stiff
Learning from Failure
Learning from Failure
Training examples
The robot is provided solely with failed examples.
It has no information about the task – no reward, no indication of
what was incorrect.
D. Grollman and A. Billard, Best Cognitive Robotics Paper Award, Int. Conf. on Robotics and Automation, ICRA 2011
Reproduction
Learning from Failure
Find a solution in a few trials
Is comparable in efficiency to classical reinforcement learning approaches
But does not need a reward!
http://lasa.epfl.ch
Teaching Robots to be Highly Reactive
http://lasa.epfl.ch
Learning a control law that ensures that you reach the target even if perturbed
and that you follow a particular dynamics
Generalizing: Learning a control law
http://lasa.epfl.ch
Coupled Dynamical Systems
Decoupled control of hand and fingers may lead to
failure when adapting to very rapid perturbations.
Coupled control of hand and fingers ensures that fingers and
hand close in a coordinated manner on the new target.
http://lasa.epfl.ch
Adaptation to perturbation of the order of a few millisecunds.
Coupled Dynamical Systems Coupled Dynamical Systems for Reach and Grasp
http://lasa.epfl.ch
Catching Objects in Flight
http://lasa.epfl.ch
Catching Objects in Flight
Extremely fast computation (object flies in half a second); re-estimation of
arm motion to adapt to noisy visual detection of object.
http://lasa.epfl.ch
STEP 1: Build a model of the graspable region on the object;
Catching Objects in Flight
http://lasa.epfl.ch
STEP 1: Build a model of the graspable region on the object; learn likelihood
of placing fingers in region of the handle from several demonstrations;
X (m)
Z (
m)
-0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05
-0.04
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0
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Z (m
)
X (m)
Catching Objects in Flight
x
z
http://lasa.epfl.ch
STEP 1: Build a model of the graspable region on the object; learn likelihood
of placing fingers in region of the handle from several demonstrations;
X (m)
Z (
m)
-0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05
-0.04
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-0.02
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0
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x
z
y
X (m)
Y (
m)
-0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05-0.04
-0.03
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0
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X (m)
z
Y (m
)
x
Catching Objects in Flight
y x
z
Z (m
)
X (m)
http://lasa.epfl.ch
If the motion of the object is complex, a simple
ballistic model is not sufficient
Needs to estimate the dynamics of object in flight
Catching Objects in Flight
STEP2: Learn a model of the translational and rotational motion of the object
http://lasa.epfl.ch
STEP2: Gather several examples
Catching Objects in Flight
Use non-linear regression model (Support
Vector Regression)
Precision (1cm, 1degree); computation
0.17-0.32 second ahead of time.
Combine with extended Kalman Filter to
tackle innacuracy of vision.
S. Kim and A. Billard, Aut. Robots, 2012
http://lasa.epfl.ch
STEP 3: Compute the region of feasible
hand postures that yield a possible grasp.
Catching Objects in Flight
http://lasa.epfl.ch
STEP 3: Compute the region of feasible hand postures that yield a possible
grasp through sampling space.
X (m)
Y (
m)
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X (m)
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m)
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Y (m)
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m)
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0
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sX
sY
-5 -4 -3 -2 -1 0 1 2 3 4
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sX
sZ
-5 -4 -3 -2 -1 0 1 2 3 4
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sY
sZ
-6 -5 -4 -3 -2 -1 0 1 2 3
-3
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Probability Contour
sX
sZ
-1.5 -1 -0.5 0 0.5 1 1.5
-1
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Position Orientation
Catching Objects in Flight
http://lasa.epfl.ch
STEP 4: Find the grasping posture by
predicting dynamics of motion and finding
most likely combination of grasping point
and feasible hand posture.
Catching Objects in Flight
http://lasa.epfl.ch
Catching Objects in Flight
STEP 5: Generate motion of hand and fingers
to catch the object at the right place using
coupled dynamical systems for hand position
and orientation and for finger motion.
http://lasa.epfl.ch
Catching a flying object
Kim, Shukla and Billard: In preparation
Catching Objects in Flight
http://lasa.epfl.ch
Catching a flying object
Kim, Shukla and Billard: In preparation
Catching Objects in Flight
Our funny robots
The lab – Class of 2011
The lab – Class of 2012
http://lasa.epfl.ch
Sponsors
Thanks to the lab – Class of 2011
Photo by Lucia Pais & Basilio Noris