Automatic end tool alignment through plane detection with a RANSAC-algorithm for robotic grasping

Camera based grasping algorithms enable the handling of unknown objects without a complete CAD model. In some scenarios, the captured information from a single view is not sufficient or no grasp is possible. For these cases, the precise realignment of the gripper is difficult because a suitable rotation is part of an infinite solution space. In this paper, we propose a framework which automatically identifies correct rotations from point clouds to adjust the gripper. We validate our approach in a virtual environment for a parallel jaw gripper with multiple isolated and grouped industrial objects

Interactive singulation of objects from a pile

Abstract—Interaction with unstructured groups of objects allows a robot to discover and manipulate novel items in cluttered environments. We present a framework for interactive singulation of individual items from a pile. The proposed framework provides an overall approach for tasks involving operation on multiple objects, such as counting, arranging, or sorting items in a pile. A perception module combined with pushing actions accumulates evidence of singulated items over multiple pile interactions. A decision module scores the likelihood of a single-item pile to a multiple-item pile based on the magnitude of motion and matching determined from the perception module. Three variations of the singulation framework were evaluated on a physical robot for an arrangement task. The proposed interactive singulation method with adaptive pushing reduces the grasp errors on non-singulated piles compared to alternative methods without the perception and decision modules. This work contributes the general pile interaction framework, a specific method for integrating perception and action plans with grasp decisions, and an experimental evaluation of the cost trade-offs for different singulation methods. I

An automated supermarket checkout system utilizing a SCARA robot : preliminary prototype development

In recent years, a number of retail stores have introduced self-checkout systems at the cash point, however these normally require a high degree of participation by the customer, often leading to requests for help by store attendees. A review of the literature has shown that the use of robots at checkout points, with their potential to reduce customer effort, has not yet been addressed. A separate literature review has shown that the four-axis SCARA robot, used extensively in the manufacturing industry due to its advantages in cost, speed and rigidity, is rarely applied to service tasks. In this work these two research gaps are being addressed. A first prototype of an automated supermarket checkout system, exploiting the advantages of the SCARA robot and including machine vision, has been developed. The system is able to recognize various items placed by the customer on a conveyor, transfer the items to a container, pack them neatly, and total the bill. Evaluation of the prototype indicates that acceptable speed and reliability of the system can be attained.peer-reviewe

Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks

Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional configuration spaces of deformables, compared to rigid objects, make manipulation difficult not only for multi-step planning, but even for goal specification. Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag". In this work, we develop a suite of simulated benchmarks with 1D, 2D, and 3D deformable structures, including tasks that involve image-based goal-conditioning and multi-step deformable manipulation. We propose embedding goal-conditioning into Transporter Networks, a recently proposed model architecture for learning robotic manipulation that rearranges deep features to infer displacements that can represent pick and place actions. We demonstrate that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Supplementary material is available at https://berkeleyautomation.github.io/bags/.Comment: See https://berkeleyautomation.github.io/bags/ for project website and code; v2 corrects some BibTeX entries, v3 is ICRA 2021 version (minor revisions

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic