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? D. Where to move? Grasp the coke can in the clutter!

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) ----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 ways of search 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 the series of capture region during the push-grasp 1. initial region of object 2. all possible poses in 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