Tactile-Based Object Recognition Using a Grasp-Centric Exploration

Jean-Philippe Roberge, Louis L'Écuyer-Lapierre, Jennifer Kwiatkowski, Philippe Nadeau, Vincent Duchaine
IEEE International Conference on Automation Science and Engineering (CASE) 2021
As humans, our grasping and manipulation skills are highly dependent on our ability to perceive tactile properties. Conversely, most of today's robotic operations still relies predominantly on visual feedback for identifying the objects that need to be grasped and handled. In this work, we study the problem of recognizing everyday objects based solely on their tactile attributes. This has a significant practical value, as it could allow object identification even when visual sensing is impossible, or assist vision in difficult contexts. Our method consists of acquiring multi-modal tactile sensing data during a quick grasp-centric exploration phase, with minimal operational cost. Our algorithm was able to recognize objects from a considerably-large set of 50 general purpose items with an accuracy of 98.1%. Moreover, we show that it is possible to reliably identify a large proportion of these objects by only analyzing the deformation pattern that they undergo during compression. Further, we study our method's ability to learn relevant tactile properties to classify new objects. We also share our tactile sensing database that contains various sensor data acquired from more than 1600 experiments, which was used for this work. Finally, we discuss the relative performance and role of each tactile modality for differentiating objects.

Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors

Abhinav Grover, Christopher Grebe, Philippe Nadeau, Jonathan Kelly
The ability to perceive object slip through tactile feedback allows humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many robotics applications, tactile sensors have yet to be widely deployed in industrial settings – part of the challenge lies in identifying slip and other key events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. These sensors have many desirable properties including high reliability and durability, and are built from very inexpensive components. We are able to achieve slip detection accuracies of greater than 91 Further, we test our detector on two robot manipulation tasks involving a variety of common objects and demonstrate successful generalization to real-world scenarios not seen during training. We show that barometric tactile sensing technology, combined with data-driven learning, is potentially suitable for many complex manipulation tasks such as slip compensation.

Tactile sensing based on fingertip suction flow for submerged dexterous manipulation

Philippe Nadeau, Michael Abbott, Dominic Melville and Hannah S. Stuart
IEEE International Conference on Robotics and Automation (ICRA) 2020
The ocean is a harsh and unstructured environment for robotic systems; high ambient pressures, saltwater corrosion and low-light conditions demand machines with robust electrical and mechanical parts that are able to sense and respond to the environment. Prior work shows that the addition of gentle suction flow to the hands of underwater robots can aid in the handling of objects during mobile manipulation tasks. The current paper explores using this suction flow mechanism as a new modality for tactile sensing; by monitoring orifice occlusion we can get a sense of how objects make contact in the hand. The electronics required for this sensor can be located remotely from the hand and the signal is insensitive to large changes in ambient pressure associated with diving depth. In this study, suction is applied to the fingertips of a two-fingered compliant gripper and suction-based tactile sensing is monitored while an object is pulled out of a pinch grasp. As a proof of concept, a recurrent neural network model was trained to predict external force trends using only the suction signals. This tactile sensing modality holds the potential to enable automated robotic behaviors or to provide operators of remotely operated vehicles with additional feedback in a robust fashion suitable for ocean deployment.