Exploratory Motion Guided Tactile Learning for Shape-Consistent Robotic Insertion

Intelligent robots are expected to do manipulation tasks relying on real-time sensing feedback. Especially, tactile sensing plays a more and more important role in precise manipulation tasks. For example, a 1 mm error while inserting a USB stick, which is hard to perceive visually, will result in a failed insertion or even break the USB stick. In this paper, to estimate and compensate residual position uncertainties during robotic insertion tasks, an exploration motion is introduced to acquire environment information by tactile sensing and a state-of-the-art transformer-based neural network is proposed to estimate the error distance from long-duration tactile sensing data. Our system is trained on over 2000 insertion trials with basic geometry shaped 3D printed objects. Without any prior knowledge, we achieve an 85% insertion success rate with average 5 attempts on 4 unseen daily objects relying only on tactile feedback acquired from our proposed exploratory motion. It is noteworthy that our designed exploration motion can provide insightful information about extrinsic contact information and our proposed learning model exceeds previous baselines in extracting useful information regarding the contact interaction between the grasped object and the environment.