Dual-density-based reweighted ℓ1\ell_{1}-algorithms for a class of ℓ0\ell_{0}-minimization problems

The optimization problem with sparsity arises in many areas of science and engineering such as compressed sensing, image processing, statistical learning and data sparse approximation. In this paper, we study the dual-density-based reweighted $\ell_{1}$-algorithms for a class of $\ell_{0}$-minimization models which can be used to model a wide range of practical problems. This class of algorithms is based on certain convex relaxations of the reformulation of the underlying $\ell_{0}$-minimization model. Such a reformulation is a special bilevel optimization problem which, in theory, is equivalent to the underlying $\ell_{0}$-minimization problem under the assumption of strict complementarity. Some basic properties of these algorithms are discussed, and numerical experiments have been carried out to demonstrate the efficiency of the proposed algorithms. Comparison of numerical performances of the proposed methods and the classic reweighted $\ell_1$-algorithms has also been made in this paper

GelSight Svelte Hand: A Three-finger, Two-DoF, Tactile-rich, Low-cost Robot Hand for Dexterous Manipulation

This paper presents GelSight Svelte Hand, a novel 3-finger 2-DoF tactile robotic hand that is capable of performing precision grasps, power grasps, and intermediate grasps. Rich tactile signals are obtained from one camera on each finger, with an extended sensing area similar to the full length of a human finger. Each finger of GelSight Svelte Hand is supported by a semi-rigid endoskeleton and covered with soft silicone materials, which provide both rigidity and compliance. We describe the design, fabrication, functionalities, and tactile sensing capability of GelSight Svelte Hand in this paper. More information is available on our website: \url{https://gelsight-svelte.alanz.info}.Comment: Submitted and accepted to IROS 2023 workshop on Visuo-Tactile Perception, Learning, Control for Manipulation and HRI (IROS RoboTac 2023

GelSight Svelte: A Human Finger-shaped Single-camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing

Camera-based tactile sensing is a low-cost, popular approach to obtain highly detailed contact geometry information. However, most existing camera-based tactile sensors are fingertip sensors, and longer fingers often require extraneous elements to obtain an extended sensing area similar to the full length of a human finger. Moreover, existing methods to estimate proprioceptive information such as total forces and torques applied on the finger from camera-based tactile sensors are not effective when the contact geometry is complex. We introduce GelSight Svelte, a curved, human finger-sized, single-camera tactile sensor that is capable of both tactile and proprioceptive sensing over a large area. GelSight Svelte uses curved mirrors to achieve the desired shape and sensing coverage. Proprioceptive information, such as the total bending and twisting torques applied on the finger, is reflected as deformations on the flexible backbone of GelSight Svelte, which are also captured by the camera. We train a convolutional neural network to estimate the bending and twisting torques from the captured images. We conduct gel deformation experiments at various locations of the finger to evaluate the tactile sensing capability and proprioceptive sensing accuracy. To demonstrate the capability and potential uses of GelSight Svelte, we conduct an object holding task with three different grasping modes that utilize different areas of the finger. More information is available on our website: https://gelsight-svelte.alanz.infoComment: Submitted and accepted to 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback

In this paper, we address the problem of using visuo-tactile feedback for 6-DoF localization and 3D reconstruction of unknown in-hand objects. We propose FingerSLAM, a closed-loop factor graph-based pose estimator that combines local tactile sensing at finger-tip and global vision sensing from a wrist-mount camera. FingerSLAM is constructed with two constituent pose estimators: a multi-pass refined tactile-based pose estimator that captures movements from detailed local textures, and a single-pass vision-based pose estimator that predicts from a global view of the object. We also design a loop closure mechanism that actively matches current vision and tactile images to previously stored key-frames to reduce accumulated error. FingerSLAM incorporates the two sensing modalities of tactile and vision, as well as the loop closure mechanism with a factor graph-based optimization framework. Such a framework produces an optimized pose estimation solution that is more accurate than the standalone estimators. The estimated poses are then used to reconstruct the shape of the unknown object incrementally by stitching the local point clouds recovered from tactile images. We train our system on real-world data collected with 20 objects. We demonstrate reliable visuo-tactile pose estimation and shape reconstruction through quantitative and qualitative real-world evaluations on 6 objects that are unseen during training.Comment: Submitted and accepted to 2023 IEEE International Conference on Robotics and Automation (ICRA 2023