Mapping Human Hands to Robotic Hands
Literature Review
HANDS.DVI Kick Off Meeting @ IIT, January 2011
Outline
Different mapping approaches:
- Fingertip mapping
- Joint-to-Joint mapping
- Object-based mapping
Fingertip Mapping
Liu, J., Zhang, Y. “Mapping human hand motion to dexterous robotic hand”
(2008)
The motion of the human hand (the master) is detected by a CyberGlove in
order to control a 4 fingers robotic hand (the slave).
A method of fingertip mapping is developed based on Virtual Fingers in
Cartesian space.
Fingertip Mapping – Virtual Finger
approach (1/2)
Arbib, Iberall (1985) “A virtual finger is a group of real
fingers acting as a single functional unit”
This concept is used to formally characterize different
fingers in an abstract way.
Thumb is functional different from the last four fingers
so human thumb is corresponding with a robot finger.
Human ring and pinkie fingers, considered as a VF, are
mapped on a single robot finger.
Fingertip Mapping – Virtual Finger
approach (2/2)
The mapping is done as follows:
p4
p45
p5
Where p4 is the ring fingertip position, p5 is the little fingertip position, k4
and k5 are the interconnection weights among different ring and little
fingers. P45 is the virtual fingertip position.
As suggested in H.Y. Hu, X. H. Gao, J. W. Li, J. Wang, and H. Liu
“Calibrating Human Hand for Teleoperating the HIT/DLR Hand” (2004), if
inverse kinematic solutions are not possible, an approximate pose for the
robotic hand is computed.
Joint-to-Joint mapping
Goldfeder C, Ciocarlie MT, Allen PK. “Dimensionality reduction for handindependent dexterous robotic grasping.” (2007)
In order to extend the synergistic
framework to a number of different robotic
hand, in this work, a joint-to-joint mapping
is adopted, based dierctly on similarities
between human and non-human hands.
Examples:
MCP joint  proximal joint
IP joint  distal joint
Abduction  spread angle (Barrett hand)
Object-based Mapping
Griffin WB, Findley RP, Turner ML, Cutkosky MR. “Calibration and Mapping
of a Human Hand for Dexterous Telemanipulation.” (2000).
This method assumes that a sphere (a virtual object) is held between
human thumb and index finger.
Size, position and orientation of the virtual object are scaled
independently to create a transformed virtual object in the robotic hand
workspace.
This modified virtual object is then used to compute the robotic fingertip
positions.
Workspace Matching (1/3)
The object size parameter is varied non linearly:
- Comfortable manipulation region: the gain
on the object size is proportional to the
size difference between the hands.
- Edges of the workspace: the gain
increases.
The modified virtual object size is computed as:
Workspace Matching (2/3)
The virtual object midpoint is computed as the midpoint between the
human thumb and index fingertip.
The midpoint is then projected on the x-y plane.
The orientation of the objected is based on the angle of this projection.
The calculated object midpoint in the human hand frame is transformed
to the robotic hand frame using a standard transform with a unity gain in
otder to allow rolling motions of robotic fingers.
Workspace Matching (3/3)
The midpoint parameter is also varied non-linearly
Around the comfortable pinch position, we wish to
map motions to the preferred manipulation region
for the robotic hand.
When the user extends his fingers away from or
towards the palm, the robot should approach its
own workspace limits.
The vertical position of the modified midpoint will be:
Object-based extended mapping
The method presented above is useful to map onto robotic
hands (such grippers) the motion of human thumb and
index fingers.
In order to extend it to movements that involves other
fingers, such as synergistic motions, our idea is to use
the Virtual Finger concept to bring us back to the case of
two-fingers motions.