1. Picking Up the Pieces: Grasp Planning via Decomposition Trees Corey Goldfeder, Peter K. Allen, Claire Lackner, Raphael Pelosoff

2. Grasp Synthesis  High dimensional, nonlinear space  configuration space = joints + pose  grasp quality is not smooth  Difficult to model analytically  Must account for dynamics, soft contacts, non-fingertip contacts, material properties  Many constraints  Obstacles, hand kinematics and scale

3. Our approach  Simulation based grasp synthesis has many advantages  Space of all grasps is too large to explore fully in simulation  We want a subspace that contains many good grasps

4. GraspIt!  Grasp simulator for both robotic and human hands  Includes kinematics, dynamics  Real time 3D visualization  Efficiently computes grasp quality Graspit!: A Versatile Simulator for Robotic Grasping, IEEE Robotics and Automation Magazine, 11.4

5. Grasping By Parts  Automatic Grasp Planning Using Shape Primitives -Miller et. al.

6. Superquadrics  Simple volumetric primitive  Small parameter space (11 dimensions)  Preserves approximate normals

7.  Segmentation and Superquadric Modeling of 3D Objects - Chevalier, Jaillet, Baskurt  We added nearest neighbor pruning to reduce complexity by a factor of n Split-Merge Decomposition

8. Decomposition Trees A model… 8 levels of decomposition… …the decomposition tree

9. Decomposition Trees  Building a tree from the bottom up  Pairwise merge of parts with least error

10. How Many Parts?  Use an error threshold?  Problem: large superquadrics can swallow important features, like handles, without much error  Solution: fixed number of parts  decompose all objects to n superquadrics  n is chosen experimentally for a given hand

11. Planner Overview  Decompose into tree with n leaves  Plan grasps on superquadrics  using entire tree, not just leaves  Simulate candidates on actual geometry, using GraspIt!  Rank results by grasp quality

12. Results  Planned multiple stable grasps for all our test objects

13. Results  Works even for objects difficult to represent with superquadrics

15. Difficulties  Assumes knowledge of object geometry  Superquadric decomposition is slow  Grasping a single part is done heuristically  Cannot plan candidates on parts from different branches of the tree

16. Do Trees Help? Without trees With trees  Without a tree, some good grasps  With a tree, many good grasps  if a grasp is unsuitable, another good grasp can be substituted

17. Contributions  Fully automatic implementation of grasping-by-parts  Abstracts away fine features  Allows multiple parts to be planned on as a group

18. Future Work  Incorporate existing SVM planner for individual superquadrics  Speed up decomposition  Questions?