Folding Knots Using a Team of Aerial Robots

From ancient times, humans have been using cables and ropes to tie, carry, and manipulate objects by folding knots. However, automating knot folding is challenging because it requires dexterity to move a cable over and under itself. In this paper, we propose a method to fold knots in midair using a team of aerial vehicles. We take advantage of the fact that vehicles are able to fly in between cable segments without any re-grasping. So the team grasps the cable from the floor, and releases it once the knot is folded. Based on a composition of catenary curves, we simplify the complexity of dealing with an infinite-dimensional configuration space of the cable, and formally propose a new knot representation. Such representation allows us to design a trajectory that can be used to fold knots using a leader-follower approach. We show that our method works for different types of knots in simulations. Additionally, we show that our solution is also computationally efficient and can be executed in real-time.Comment: International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, Oct 23 - Oct. 27, 202

Flexible Object Manipulation

Flexible objects are a challenge to manipulate. Their motions are hard to predict, and the high number of degrees of freedom makes sensing, control, and planning difficult. Additionally, they have more complex friction and contact issues than rigid bodies, and they may stretch and compress. In this thesis, I explore two major types of flexible materials: cloth and string. For rigid bodies, one of the most basic problems in manipulation is the development of immobilizing grasps. The same problem exists for flexible objects. I have shown that a simple polygonal piece of cloth can be fully immobilized by grasping all convex vertices and no more than one third of the concave vertices. I also explored simple manipulation methods that make use of gravity to reduce the number of fingers necessary for grasping. I have built a system for folding a T-shirt using a 4 DOF arm and a fixed-length iron bar which simulates two fingers. The main goal with string manipulation has been to tie knots without the use of any sensing. I have developed single-piece fixtures capable of tying knots in fishing line, solder, and wire, along with a more complex track-based system for autonomously tying a knot in steel wire. I have also developed a series of different fixtures that use compressed air to tie knots in string. Additionally, I have designed four-piece fixtures, which demonstrate a way to fully enclose a knot during the insertion process, while guaranteeing that extraction will always succeed

Data-driven robotic manipulation of cloth-like deformable objects : the present, challenges and future prospects

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms.Publisher PDFPeer reviewe

Survey on model-based manipulation planning of deformable objects

A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modelling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.Preprin

Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation

Manipulation of deformable objects, such as ropes and cloth, is an important but challenging problem in robotics. We present a learning-based system where a robot takes as input a sequence of images of a human manipulating a rope from an initial to goal configuration, and outputs a sequence of actions that can reproduce the human demonstration, using only monocular images as input. To perform this task, the robot learns a pixel-level inverse dynamics model of rope manipulation directly from images in a self-supervised manner, using about 60K interactions with the rope collected autonomously by the robot. The human demonstration provides a high-level plan of what to do and the low-level inverse model is used to execute the plan. We show that by combining the high and low-level plans, the robot can successfully manipulate a rope into a variety of target shapes using only a sequence of human-provided images for direction.Comment: 8 pages, accepted to International Conference on Robotics and Automation (ICRA) 201

HANDLOOM: Learned Tracing of One-Dimensional Objects for Inspection and Manipulation

Tracing - estimating the spatial state of - long deformable linear objects such as cables, threads, hoses, or ropes, is useful for a broad range of tasks in homes, retail, factories, construction, transportation, and healthcare. For long deformable linear objects (DLOs or simply cables) with many (over 25) crossings, we present HANDLOOM (Heterogeneous Autoregressive Learned Deformable Linear Object Observation and Manipulation), a learning-based algorithm that fits a trace to a greyscale image of cables. We evaluate HANDLOOM on semi-planar DLO configurations where each crossing involves at most 2 segments. HANDLOOM makes use of neural networks trained with 30,000 simulated examples and 568 real examples to autoregressively estimate traces of cables and classify crossings. Experiments find that in settings with multiple identical cables, HANDLOOM can trace each cable with 80% accuracy. In single-cable images, HANDLOOM can trace and identify knots with 77% accuracy. When HANDLOOM is incorporated into a bimanual robot system, it enables state-based imitation of knot tying with 80% accuracy, and it successfully untangles 64% of cable configurations across 3 levels of difficulty. Additionally, HANDLOOM demonstrates generalization to knot types and materials (rubber, cloth rope) not present in the training dataset with 85% accuracy. Supplementary material, including all code and an annotated dataset of RGB-D images of cables along with ground-truth traces, is at https://sites.google.com/view/cable-tracing

Validation of a Net Active Debris Removal Simulator within Parabolic Flight Experiment

Currently space debris is recognized as a major risk for space missions. In this frame it is involved the Patender project (Net parametric characterization and parabolic flight). The goal of this ESA funded activity (ending by March 2014) is to develop a confident mean to further investigate, develop and validate the concept of using nets for actively removing space debris of different characteristics. The net simulator will be validated in a parabolic flight experiment where microgravity conditions can be reached during some few tens of seconds. Different net shapes (pyramidal/planar) will be launched using a pneumatic-based dedicated mechanism in order to simulate the capture of a large space debris. High-speed motion cameras will record the experiment in order to allow the 3D reconstruction of the deployment and wrapping around the target phases and the validation of the software simulator

Making grammars: From computing with shapes to computing with things

Recent interest in making and materiality spans from the humanities and social sciences to engineering, science, and design. Here, we consider making through the lens of a unique computational theory of design: shape grammars. We propose a computational theory of making based on the improvisational, perception and action approach of shape grammars and the shape algebras that support them. We modify algebras for the materials (basic elements) of shapes to define algebras for the materials of objects, or things. Then we adapt shape grammars for computing shapes to making grammars for computing things. We give examples of making grammars and their algebras. We conclude by reframing designing and making in light of our computational theory of making

Indentation of an elastic arch on a frictional substrate: Pinning, unfolding and snapping

We investigate the morphology and mechanics of a naturally curved elastic arch loaded at its center and frictionally supported at both ends on a flat, rigid substrate. Through systematic numerical simulations, we classify the observed behaviors of the arch into three distinct types of configurations in terms of the arch geometry and the coefficient of static friction with the substrate. A linear theory is developed based on a planar elastica model combined with Amontons-Coulomb's frictional law, which quantitatively explains the numerically constructed phase diagram. The snapping transition of a loaded arch in a sufficiently large indentation regime, which involves a discontinuous force jump, is numerically observed. The proposed model problem allows a fully analytical investigation and demonstrates a rich variety of mechanical behaviors owing to the interplay between elasticity, geometry, and friction. This study provides a basis for understanding more common but complex systems, such as a cylindrical shell subjected to a concentrated load and simultaneously supported by frictional contact with surrounding objects