Teaching Robots toDrink, Relax and Play catch
Factory Robots
Factory RobotsFactory robots live in a human-less world.
Factory RobotsFactory robots function in a world that is fully predetermined,where there is no room for change.
Going into the real worldUnlike industrial settings, the worldin which we live changes all thetime: Cannot predict all ci...
Robots that deal with uncertaintyCommercial airplanes fly autonomously to a very large extent.      They can recover from ...
Robots that deal with uncertaintyAutomobile industry and governments support research            to build fully autonomous...
Uncertainty in home environment  No two apartments    look the sameAnd the same apartmentcan change appearancefrom one day...
Variability in task definitionWhat does it mean to grate carrots?More than one way to do this.                            ...
Learning from Human Demonstrations
Learning from Human DemonstrationsLearning a skill is more than simply replaying a trajectory.        It requires to under...
Learning from Human Demonstrations     Learning a skill is more than simply replaying a trajectory.             It require...
Learning from Human Demonstrations
Teaching robot how to adapt to perturbations
Teaching robots to be less stiffBeing stiff is not always good  How to teach a robot to relax…Low stiffness when carrying...
Teaching robots to be less stiffBeing stiff is not always good  How to teach a robot to relax…Shaking the robot: A natura...
Teaching robots to be less stiffAfter training the robot manages to adapt naturally whenrequired and remains stiff when re...
Learning from Failure
Learning from FailureThe robot is provided solely with failed examples.It has no information about the task – no reward, n...
Learning from Failure                        Find a solution in a few trialsIs comparable in efficiency to classical reinf...
Teaching Robots to be Highly Reactive                                  http://lasa.epfl.ch
Generalizing: Learning a control lawLearning a control law that ensures that you reach the target even if perturbed       ...
Coupled Dynamical SystemsCoupled control of hand andand fingers may lead fingers and Decoupled control of hand fingers ens...
CoupledCoupled Dynamical Systems Grasp        Dynamical Systems for Reach andAdaptation to perturbation of the order of a ...
Catching Objects in Flight                             http://lasa.epfl.ch
Catching Objects in FlightExtremely fast computation (object flies in half a second); re-estimation ofarm motion to adapt ...
Catching Objects in FlightSTEP 1: Build a model of the graspable region on the object;                                    ...
Catching Objects in Flight STEP 1: Build a model of the graspable region on the object; learn likelihood of placing finger...
Catching Objects in Flight STEP 1: Build a model of the graspable region on the object; learn likelihood of placing finger...
Catching Objects in FlightSTEP2: Learn a model of the translational and rotational motion of the objectIf the motion of th...
Catching Objects in FlightSTEP2: Gather several examplesUse non-linear regression model (SupportVector Regression)Precisio...
Catching Objects in FlightSTEP 3: Compute the region of feasiblehand postures that yield a possible grasp.                ...
Catching Objects in FlightSTEP 3: Compute the region of feasible hand postures that yield a possiblegrasp through sampling...
Catching Objects in FlightSTEP 4: Find the grasping posture bypredicting dynamics of motion and findingmost likely combina...
Catching Objects in FlightSTEP 5: Generate motion of hand and fingersto catch the object at the right place usingcoupled d...
Catching Objects inobject Catching a flying Flight   Kim, Shukla and Billard: In preparation   http://lasa.epfl.ch
Catching Objects inobject Catching a flying Flight   Kim, Shukla and Billard: In preparation   http://lasa.epfl.ch
Our funny robots
The lab – Class of 2011
The lab – Class of 2012
Sponsors           http://lasa.epfl.ch
Thanks to the lab – Class of 2011Photo by Lucia Pais & Basilio Noris
Upcoming SlideShare
Loading in...5
×

Robots and Humans - Aude Billard

833

Published on

Published in: Lifestyle, Technology, Business
2 Comments
0 Likes
Statistics
Notes
  • wooOOOOOOOOOOOOOOW.......super
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • It was very interesting to know what happen in the world
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
  • Be the first to like this

No Downloads
Views
Total Views
833
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
15
Comments
2
Likes
0
Embeds 0
No embeds

No notes for slide

Robots and Humans - Aude Billard

  1. 1. Teaching Robots toDrink, Relax and Play catch
  2. 2. Factory Robots
  3. 3. Factory RobotsFactory robots live in a human-less world.
  4. 4. Factory RobotsFactory robots function in a world that is fully predetermined,where there is no room for change.
  5. 5. Going into the real worldUnlike industrial settings, the worldin which we live changes all thetime: Cannot predict all circumstances Need to react rapidly and appropriately
  6. 6. Robots that deal with uncertaintyCommercial airplanes fly autonomously to a very large extent. They can recover from turbulences automatically.
  7. 7. Robots that deal with uncertaintyAutomobile industry and governments support research to build fully autonomous cars.
  8. 8. Uncertainty in home environment No two apartments look the sameAnd the same apartmentcan change appearancefrom one day to the next
  9. 9. Variability in task definitionWhat does it mean to grate carrots?More than one way to do this. More than one tool to perform the task.
  10. 10. Learning from Human Demonstrations
  11. 11. Learning from Human DemonstrationsLearning 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.
  12. 12. 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.K. Kronander, M. Khansari and A. Billard, JTSC Best Paper Award, Int. Conf. on Intelligent and Robotics Systems, IROS 2011
  13. 13. Learning from Human Demonstrations
  14. 14. Teaching robot how to adapt to perturbations
  15. 15. Teaching robots to be less stiffBeing stiff is not always good  How to teach a robot to relax…Low stiffness when carrying the liquid High stiffness when pouring the liquid http://lasa.epfl.ch
  16. 16. Teaching robots to be less stiffBeing stiff is not always good  How to teach a robot to relax…Shaking the robot: A natural method to teach a robot to relax. http://lasa.epfl.ch
  17. 17. Teaching robots to be less stiffAfter training the robot manages to adapt naturally whenrequired and remains stiff when required. http://lasa.epfl.ch
  18. 18. Learning from Failure
  19. 19. Learning from FailureThe robot is provided solely with failed examples.It has no information about the task – no reward, no indication ofwhat was incorrect. Training examples Reproduction D. Grollman and A. Billard, Best Cognitive Robotics Paper Award, Int. Conf. on Robotics and Automation, ICRA 2011
  20. 20. Learning from Failure Find a solution in a few trialsIs comparable in efficiency to classical reinforcement learning approaches But does not need a reward!
  21. 21. Teaching Robots to be Highly Reactive http://lasa.epfl.ch
  22. 22. Generalizing: Learning a control lawLearning a control law that ensures that you reach the target even if perturbed and that you follow a particular dynamics http://lasa.epfl.ch
  23. 23. Coupled Dynamical SystemsCoupled control of hand andand fingers may lead fingers and Decoupled control of hand fingers ensures that tohand close in a coordinated manner perturbations. failure when adapting to very rapid on the new target. http://lasa.epfl.ch
  24. 24. CoupledCoupled Dynamical Systems Grasp Dynamical Systems for Reach andAdaptation to perturbation of the order of a few millisecunds. http://lasa.epfl.ch
  25. 25. Catching Objects in Flight http://lasa.epfl.ch
  26. 26. Catching Objects in FlightExtremely fast computation (object flies in half a second); re-estimation ofarm motion to adapt to noisy visual detection of object. http://lasa.epfl.ch
  27. 27. Catching Objects in FlightSTEP 1: Build a model of the graspable region on the object; http://lasa.epfl.ch
  28. 28. Catching Objects in Flight 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; z x 0.04 0.03 0.02 0.01Z (m) Z (m) 0 -0.01 -0.02 -0.03 -0.04 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 X (m) X (m) http://lasa.epfl.ch
  29. 29. Catching Objects in Flight 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; z x y z x y 0.04 0.04 0.03 0.03 0.02 0.02 0.01 0.01 Y (m)Z (m) Z (m) Y (m)z 0 -0.01 0 -0.01 -0.02 -0.02 -0.03 -0.03 -0.04 -0.04 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 X (m) X (m) x X (m) X (m) http://lasa.epfl.ch
  30. 30. Catching Objects in FlightSTEP2: Learn a model of the translational and rotational motion of the objectIf the motion of the object is complex, a simpleballistic model is not sufficient Needs to estimate the dynamics of object in flight http://lasa.epfl.ch
  31. 31. Catching Objects in FlightSTEP2: Gather several examplesUse non-linear regression model (SupportVector Regression)Precision (1cm, 1degree); computation0.17-0.32 second ahead of time.Combine with extended Kalman Filter totackle innacuracy of vision. http://lasa.epfl.chS. Kim and A. Billard, Aut. Robots, 2012
  32. 32. Catching Objects in FlightSTEP 3: Compute the region of feasiblehand postures that yield a possible grasp. http://lasa.epfl.ch
  33. 33. Catching Objects in FlightSTEP 3: Compute the region of feasible hand postures that yield a possiblegrasp through sampling space. Probability Contour 0.5 3 0.4 2 0.3 0.2 1 0.1 Y (m) sY 0 0 -1 -0.1 -0.2 -2 -0.3 -3 -0.4 -0.6 -0.4 -0.2 0 0.2 0.4 -5 -4 -3 -2 -1 0 1 2 3 4 X (m) sX 0.5 0.4 3 0.3 2 0.2 1 Z (m) 0.1 sZ 0 0 -0.1 -1 -0.2 -2 -0.3 -0.4 -3 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 X (m) -5 -4 -3 -2 -1 0 1 2 3 4 sX 3 0.45 0.5 1 0.8 2 0.4 0.4 0.6 0.4 1 0.35 0.3 Z (m) 0.2 0.3 sZ 0 sZ 0.2 0 -0.2 0.25 -1 0.1 -0.4 0.2 -0.6 -2 0 -0.8 0.15 -3 -1 0.1 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 -6 -5 -4 -3 -2 -1 0 1 2 3 Y (m) -1.5 -1 -0.5 0 0.5 1 1.5 sY Position Orientation sX http://lasa.epfl.ch
  34. 34. Catching Objects in FlightSTEP 4: Find the grasping posture bypredicting dynamics of motion and findingmost likely combination of grasping pointand feasible hand posture. http://lasa.epfl.ch
  35. 35. Catching Objects in FlightSTEP 5: Generate motion of hand and fingersto catch the object at the right place usingcoupled dynamical systems for hand positionand orientation and for finger motion. http://lasa.epfl.ch
  36. 36. Catching Objects inobject Catching a flying Flight Kim, Shukla and Billard: In preparation http://lasa.epfl.ch
  37. 37. Catching Objects inobject Catching a flying Flight Kim, Shukla and Billard: In preparation http://lasa.epfl.ch
  38. 38. Our funny robots
  39. 39. The lab – Class of 2011
  40. 40. The lab – Class of 2012
  41. 41. Sponsors http://lasa.epfl.ch
  42. 42. Thanks to the lab – Class of 2011Photo by Lucia Pais & Basilio Noris
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×