ICRA 2011 Paper Abstract


Paper WeA111.4

Dragiev, Stanimir (TU Berlin), Toussaint, Marc (TU Berlin), Gienger, Michael (Honda Research Institute Europe)

Gaussian Process Implicit Surfaces for Shape Estimation and Grasping

Scheduled for presentation during the Regular Sessions "Grasping I" (WeA111), Wednesday, May 11, 2011, 09:05−09:20, Room 5F

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 5, 2020

Keywords Grasping, Sensor Fusion, Motion Control of Manipulators


The choice of an adequate object shape representation is critical for efficient grasping and robot manipulation. A good representation has to account for two requirements: it should allow uncertain sensory fusion in a probabilistic way and it should serve as a basis for efficient grasp and motion generation. We consider Gaussian process implicit surface potentials as object shape representations. Sensory observations condition the Gaussian process such that its posterior mean defines an implicit surface which becomes an estimate of the object shape. Uncertain visual, haptic and laser data can equally be fused in the same Gaussian process shape estimate. The resulting implicit surface potential can then be used directly as a basis for a reach and grasp controller, serving as an attractor for the grasp end-effectors and steering the orientation of contact points. Our proposed controller results in a smooth reach and grasp trajectory without strict separation of phases. We validate the shape estimation using Gaussian processes in a simulation on randomly sampled shapes and the grasp controller on a real robot with 7DoF arm and 7DoF hand.



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