learning and control with movement primitives in multiple ...€¦ · coordinate system 2: this is...
Post on 11-Sep-2020
0 Views
Preview:
TRANSCRIPT
Sylvain Calinon Robot Learning & Interaction Group Idiap Research Institute Martigny, Switzerland
Learning and control with movement primitives in multiple coordinate systems
RIBA healthcare robot, RIKEN i-limb prosthetics, TouchBionics
Humanoid diver, Stanford Robonaut, NASA
Learning from demonstration as an intuitive interface to transfer skills to robots
Statistical learning dynamical systems +
We look for a modular representation of movements and skills that can learn from wide-ranging data, that can adapt to new situations in a fast manner, that can exploit the robot embodiment, and that is robust to perturbations.
System plant
Model predictive control (MPC) Do it smoothly! Track path!
How to solve this cost function? • Pontryagin’s maximum principle Riccati equation
• Dynamic programming • Linear algebra with stacked
vectors [Tanwani and Calinon, IEEE RA-L 1(1), 2016]
state variable (position+velocity) control command (acceleration) tracking weight matrix control weight matrix
System plant
Model predictive control (MPC) Do it smoothly! Track path!
[Tanwani and Calinon, IEEE RA-L 1(1), 2016]
Model predictive control (MPC)
[Tanwani and Calinon, IEEE RA-L 1(1), 2016]
Sharing of synergies with:
Model predictive control (MPC)
[Tanwani and Calinon, IEEE RA-L 1(1), 2016]
Minimal intervention Safe/compliant robot controller
Transition and state duration
Stepwise sequence with:
Transfer of controllers from demonstration
Demonstration Reproduction
[Calinon, Intelligent Service Robotics 9(1), 2016]
Hold
ing
a cu
p ho
rizon
tally
Bi
man
ual
coor
dina
tion
in a new situation…
Extension to multiple coordinate systems
Coordinate system 1: This is where I expect
data to be located!
Coordinate system 2: This is where I expect
data to be located!
[Calinon, Bruno and Caldwell, ICRA’2014][Calinon, HFR’2016]
Product of linearly transformed Gaussians
Track path in coordinate system j
MPC considering multiple coordinate systems
Do it smoothly!
1
2
2 2
2
1
2
Set of demonstrations Reproduction in new situation
New position and orientation of coordinate
systems 1 and 2
Two candidate coordinate systems (P=2)
[Calinon, Bruno and Caldwell, ICRA’2014][Calinon, HFR’2016]
MPC considering multiple coordinate systems
1
2
In many robotics problems, the parameters describing the task or situation can be recast as some form of coordinate systems or locally linear transformations
[Calinon, Bruno and Caldwell, ICRA’2014][Calinon, HFR’2016]
Track path in coordinate system j Do it smoothly!
MPC considering multiple coordinate systems
Learning of a controller that adapts to new situations while regulating its gains according to the precision and coordination required by the task
In many robotics problems, the parameters describing the task or situation can be recast as some form of coordinate systems or locally linear transformations
Track path in coordinate system j Do it smoothly!
[Calinon, Bruno and Caldwell, ICRA’2014][Calinon, HFR’2016]
Adaptation of movements to different shapes
[Calinon, Alizadeh and Caldwell, IROS’2013]
Candidate coordinate
system
[Silverio et al., IROS’2015]
[Rozo et al., IROS’2015]
[Rozo et al., IEEE T-RO 32(3), 2016]
Bimanual coordination and co-manipulation
Dr Leonel Rozo João Silvério
Joint space constraints & tasks prioritization
Task space (operational space)
Joint space (configuration space)
Task parameters as Jacobian operators
Task parameters as null space projection operators
[Silverio, Calinon, Rozo and Caldwell (submitted)] [Calinon, ISRR’2015]
Demonstration on COMAN
Left
han
d
prio
rity
Righ
t han
d
prio
rity
Reproduction on WALKMAN
Learning from demonstration can be applied to various forms of robots and applications
(2015-2018)
(2015-2018)
(2012-2015)
Stiff Compliant
[Bruno, Calinon, Malekzadeh and Caldwell, ICIRA, LNCS 9246, 2015]
s=0 s=1
Continuous arm index s MPC for continuum robots
Flexible robots in minimal invasive surgery
Collaboration letting the surgeon control the tip, while the robot exploits the remaining degrees of freedom that do not interfere with the control of the tip
Insertion Retraction
[Bruno, Calinon and Caldwell, Autonomous Robots, 2016]
Robotic dressing assistance
Dressing assistance for: - Putting on a coat - Putting on shoes Requires to extend movement primitives to reaction, force and impedance primitives
Telemanipulation with underwater robot
Onshore Offshore
Same model (TP-HSMM) used for classification on the one side, and synthesis on the other side, with local adaptation to the task parameters
Being skillful =
exploiting variability and
correlation
Needs correction
Does not need correction
Recognition & synthesis of motion primitives
Semi-autonomous teleoperation as
a form of human-robot collaboration
[Tanwani and Calinon, IEEE RA-L 1(1), 2016]
Telemanipulation with underwater robot
• Model predictive control (MPC) can be smoothly combined with learning from demonstration (LfD) for both planning and control problems
• The parameters of the cost function in MPC can be learned from demonstration, with weights as full precision matrices, instead of predefining those manually as diagonal matrices
• LfD can be extended to the consideration of multiple coordinate systems and to the learning of controllers Minimal intervention strategy that is safer for the users
• Extending LfD to collaborative skills and teleoperation provides new perspectives in shared control by exploiting the learned task variations and task synergies to cope with perturbations
Conclusion
Contact: sylvain.calinon@idiap.ch http://calinon.ch
Source codes: http://www.idiap.ch/software/pbdlib/
Ajay Tanwani
Emmanuel Pignat Noémie Jaquier
Dr Ioannis Havoutis
Photo: Basilio Noris
Collaborators at Idiap:
top related