BiGS: BioTac Grasp Stability Dataset
Autonomous grasping of unknown objects is a fundamental requirement for robots performing manipulation tasks in real world environments. Even though there has been a lot of progress in the area of grasping, it is still considered an open challenge and even the state of the art grasping methods may result in failures [1] A reliable prediction of grasp stability helps to avoid such failures and provides an option to re grasp the object safely. Since the majority of grasping failures happen at the contact points, which are occluded for vision systems, tactile feedback plays a major role for predicting grasp stability. The human inspired biomimmetic tactile sensor (BioTac) [2] is equipped with a 19 electrode array and a hydroacoustic sensor surrounded by silicon skin inflated with incompressible and conductive liquid. This design provides rich tactile feedback similar to the slowly adapting and fastadapting afferents present in the human skin [3] Latest developments in classification algorithms [4] allow us to explore the potential of large amounts of data from these sensors. Our goal is to provide a publicly accessible grasp stability dataset collected using the BioTacs and, thus, enable further development of algorithms capable of reliable grasp stability prediction.