5 accepted papers
Vision-based tactile sensors typically utilize a deformable elastomer and a camera mounted above to provide high-resolution image observations of contacts. Obtaining accurate volumetric meshes for the deformed elastomer can provide direct contact information and benefit robotic grasping and manipula…
Recovering rigid registration between successive camera poses lies at the heart of 3D reconstruction, SLAM and visual odometry. Registration relies on the ability to compute discriminative 2D features in successive camera images for determining feature correspondences, which is very challenging in f…
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success in many complex tasks, these algorithms need a large number of samples to learn meaningful policies. In this letter, we
In this paper, we present a novel approach for classification of unseen object instances from interactive tactile feedback. Furthermore, we demonstrate the utility of a low resolution tactile sensor array for tactile perception that can potentially close the gap between vision and physical contact f…
Learning robot tasks or controllers using deep reinforcement learning has been proven effective in simulations. Learning in simulation has several advantages. For example, one can fully control the simulated environment, including halting motions while performing computations. Another advantage when…