Humanoid Robot Grasping with a Soft Gripper Through a Learned Inverse Model of a Central Pattern Generator and Tactile Servoing

Abstract

Grasping and manipulation are essential skills that humanoid robots need in order to operate in the human environment. Model-based methods require a precise calibration and suffer from high order non-linearity. While, neural-based representations does not require a dedicated calibration process to solve these tasks. However, some suffer from high generalization error that reduces the accuracy or require large-scale data collection. The role of sensory feedback is therefore important to adapt the action. We present a control framework to learn grasping with a soft gripper attached to a humanoid robot arm. The inverse kinematic model of the arm is acquired through motor babbling of a central pattern generator and encoded by a feed-forward neural network. To overcome the generalization error we provide the gripper with a tactile sensors array at each finger. The tactile servoing is used to correct the action before grasping. The proposed model has been tested in simulation, and on the real robot where a soft sensory gripper was used to interact with a human subject (Tactile Servoing). Successful grasping was achieved thanks to the integration of a learned inverse model with the sensory feedback.