Transcript A Framework for Push-grasping in Clutter

A Framework for Push-grasping in Clutter
Mehmet R. Dogar
Siddhartha S. Srinivasa
Zheng Yang
2012-10-15
Outline
Tasks in Cluttered Environment
The planning Framework
The Action Library
Conclusion
Limitation and Future work
Tasks in Cluttered Environment
Grasp the coke can in the clutter!
Tasks in Cluttered Environment
Issues:
A. Which to move? In what order?
B. How to deal with uncertainty?
C. With what action?
Grasp the coke can in the clutter!
D. Where to move?
The planning Framework
A. Which to move?
1. Attempt to pick the goal object, allowing penetrating into
others
2. Identify the penetrated objects and add them on the
move list (FindPenetrate)
3. plan to move the object on the move list and iterate
The planning Framework
A. Which to move?
Backward Planning (recursive call)
Monotone Planning (avoidVol)
The planning Framework
B. How to deal with uncertainty?
Determine the initial uncertainty from perception, the
region:
U(0)
Track the uncertainty during manipulation -- the evolution
of region U(0) :
v[0,1]
Calculate the volume (Volume):
Volume(o,v)
The planning Framework
C. With what action?
The Action Library
Generally an action (a):
Or transit action (Goto)
The planning Framework
D. Where to move?
The goal object
The object on the movelist
The NGR (negative goal region): the sum of the
volume of space used by all the previous planned
actions
The goal region G is
StablePose-NGR
The planning Framework
Reconfigure:
a. search the action library
b. generate an action
if failed then continue at a
c. generate transit plan(Goto)
if failed then continue at a
----combine b and c as the plan
d. calculate the action volume of the plan
e. determine the penetrated objects
if none then return the plan
f. update the NGR and avoidVol
g. recursive call of reconfigure of the next
object on the list
if done
add the recursive plan before plan
if fail then search again
----if all actions tried then return empty
The Action Library
Push grasp
Sweep
parameter
PG(ph, a, d)
S(ph, d)
ways of search
36 discrete direction, v
lateral offset, l
9 sets of aperture, a
Not specified in the
paper
capture region
evolution of
uncertainty region
1. initial region of
the series of capture
object
region during the push2. all possible poses in
grasp
contact with the hand
The Action Library
•Make sure the initial uncertainty region is in
the capture region of the action
•During Push-grasp, minimize the penetrated
object by searching different values of v, the
direction to grasp
•Calculate the volumn by sampling from the
uncertainty region and perform the Volumn
operator
•Sweeping uncertainty could be large
The Action Library
Other actions:
Goto:
Search the configuration space of the arm
using Constrained Bi-directional RRT planner
Pick up:
As a Push-grasp followed by a Goto
Conclusion
A new framework for planning beyond
traditional pick- and-place actions
Pushing can manipulate large obstacles
Consevative by considering the
uncertainty
Limitation and Future work
Sweeping will affect the efficiency because the large uncertainty
region results in more object in the movelist, especially for the tight
space
Open loop solution brings larger uncertainty without sensor
feedback between steps
Might consider dynamic obstacles
Pushing is limited on a large plane surface, what if the object is on a
small plane, such as stacked casserole in the fridge
Might thought coordination of two robot arms