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2. Robust Learning of Tactile Force Estimation through Robot Interaction
 
 # Robust Learning of Tactile Force Estimation through Robot Interaction

  ![](/sites/default/files/styles/wide/public/publications/tactile.jpg?itok=BLhSKXE5)

 Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust model that maps tactile sensor signals to force. We specifically explore learning a mapping for the SynTouch BioTac sensor via neural networks. We propose a voxelized input feature layer for spatial signals and leverage information about the sensor surface to regularize the loss function. To learn a robust tactile force model that transfers across tasks, we generate ground truth data from three different sources: (1) the BioTac rigidly mounted to a force torque~(FT) sensor, (2) a robot interacting with a ball rigidly attached to the same FT sensor, and (3) through force inference on a planar pushing task by formalizing the mechanics as a system of particles and optimizing over the object motion. A total of 140k samples were collected from the three sources. We achieve a median angular accuracy of 3.5 degrees in predicting force direction (66% improvement over the current state of the art) and a median magnitude accuracy of 0.06 N (93% improvement) on a test dataset. Additionally, we evaluate the learned force model in a force feedback grasp controller performing object lifting and gentle placement.



 ## Authors



[Balakumar Sundaralingam](/person/balakumar-sundaralingam)

Alexander Lambert (NVIDIA, Georgia Tech)

Ankur Handa (NVIDIA)

Byron Boots (NVIDIA)

[Tucker Hermans](/person/tucker-hermans)

[Stan Birchfield](/person/stan-birchfield)

Nathan Ratliff (NVIDIA)

Dieter Fox (NVIDIA)

 

 

 ## Publication Date



Wednesday, May 1, 2019

 

 ## Published in



ICRA 2019

 

 ## Research Area



[Robotics](/research-area/robotics)

 

 

 ## External Links



[arXiv paper](https://arxiv.org/abs/1810.06187)

[Website](https://sites.google.com/view/tactile-force)