manipulation under uncertainty (or: executing planned grasps robustly) kaijen hsiao tomás...
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Manipulation Under Uncertainty(or: Executing Planned Grasps Robustly)
Kaijen HsiaoTomás Lozano-PérezLeslie KaelblingComputer Science and Artificial Intelligence Lab, MITNEMS 2008
Manipulation Planning
If you know all shapes and positions exactly, you can generate a trajectory that will work
Moderate Uncertainty (not groping blindly) Initial conditions (ultimately from vision)
Object shape is roughly known (contacted vertices should be within ~1 cm of actual positions)
Object is on table and pose (x, y, rotation) is roughly known (center of mass std ~5 cm, 30 deg)
Online sensing: robot proprioception tactile sensors on fingers/hand
Planned/demonstrated trajectories (that would work under zero uncertainty) given
Model uncertainty explicitly “Belief state”: probability distribution over positions of
object relative to robot
Use online sensing to update belief state throughout manipulation (SE)
Select manipulation actions based on belief state (π)
Controller
SE
Environment
belief
actionsensing
State Estimation Transition Model: how robot actions affect the
state Do we move the object during the grasp
execution? (currently, any contact spreads out the belief state somewhat)
Observation Model: P(sensor input | state) How consistent are various object positions with
the current sensory input (robot pose and touch)? Bayes’ Rule
Control: Three approaches Formulate as a POMDP, solve for optimal policy
Continuous, multi-dimensional state, action, observation spaces
->Wildly intractable Find most likely state, plan trajectory, execute
Bad if rest of execution is open loop Maybe good if replanning is continuous, but too slow for
execution-time Will not select actions to gain information
Our approach: define new robust primitives, use information state to select plan, execute
Robust Motion PrimitiveMove-until(goal, condition):
Repeat until belief state condition is satisfied: Assume object is in its most likely location Guarded move to object-relative goal If contact is made:
Undo last motion Update belief state
Termination conditions: Claims success: robot believes, with high
probability, that it is near the object-relative goal Claims failure: some number of attempts have not
achieved the belief condition
Executing a trajectory
Given a sequence of way-points in a trajectory Attempt to execute each one robustly using
move-until
So, now we can try to close the gripper on the box:
The Approach
belief
updateworld
policy
strategy selector
most likely state
commandgenerator
relative motion robot
commands
sensor observations
current belief
Trajectories (grasp, poke, …)
Strategy Selector
Planner to automatically pick good strategies based on start uncertainties and goals Simulate all particles forward using selected robot
movements, including tipping probabilities (tipping = failure)
Group into qualitatively similar outcomes Use forward search to select trajectories/info-
gathering actions
Currently use hand-written conditions on belief state
Brita Results
Increasing uncertainty
0
10
20
30
40
50
60
70
80
90
100
loc 1, rot 3 loc 3, rot 9 loc 5, rot 15 loc 5, rot 30
Uncertainty standard dev (cm in each dir, deg rot)
Per
cen
t g
rasp
ed c
orr
ectl
y
demo with perfect info,robot stuck
MLS controller withcontact grasp, static robot
most likely state controllerplain, static robot
guarded moves (no belief)
demo with imperfect info
MLS with 2 info-grasps,robot moving
MLS with 1 info-grasp,robot moving
MLS with 2 info-grasps,static robot
Related Work Grasp planning without regard to uncertainty (can
be used as input to this research) (Lozano-Perez et al, 1992, Saxena et al, 2008)
Finding a fixed trajectory that is likely to succeed under uncertainty (Alterovitz et al. 2007, Burns and Brock 2007, Melchior and Simmons 2007, Prentice and Roy 2007)
Visual servoing (tons of work) Using tactile sensors to precisely locate object
before grasping (Petrovskaya et al. 2006) Regrasping to find stable grasp positions (Platt,
Fagg, Grupen, 2002) POMDPs for grasping (Hsiao et al. 2007)
Current Work
Real robot results (7-DOF Barrett Arm/Hand and Willow Garage PR2)
Automatic strategy selection
Key Ideas Belief-based strategy:
Maintain a belief state (updated based on actions and observations)
Express your actions relative to the current best state estimate
Choose strategies based on higher-order properties of your belief state (variance, bimodality, etc).
Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant No. 0712012. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Box Results
Goal: 1 cm x, 1 cm y, 6 degrees theta
Object uncertainty: standard deviations of 5 cm x, 5 cm y, 30 degrees theta
Mean state controller with info-grasp
120/122, 98.4%