Robotic Perception-motion Synergy for Novel Rope Wrapping Tasks

This paper introduces a novel and general method to address the problem of using a general-purpose robot manipulator with a parallel gripper to wrap a deformable linear object (DLO), called a rope, around a rigid object, called a rod, autonomously. Such a robotic wrapping task has broad potential applications in automotive, electromechanical industries construction manufacturing, etc., but has hardly been studied. Our method does not require prior knowledge of the physical and geometrical properties of the objects but enables the robot to use real-time RGB-D perception to determine the wrapping state and feedback control to achieve high-quality results. As such, it provides the robot manipulator with the general capabilities to handle wrapping tasks of different rods or ropes. We tested our method on 6 combinations of 3 different ropes and 2 rods. The result shows that the wrapping quality improved and converged within 5 wraps for all test cases

Robot Learning-Based Pipeline for Autonomous Reshaping of a Deformable Linear Object in Cluttered Backgrounds

open2noThis work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program as part of RIA Project Robotic tEchnologies for the Manipulation of cOmplex DeformablE Linear objects (REMODEL) under Grant 870133.In this work, the robotic manipulation of a highly Deformable Linear Object (DLO) is addressed by means of a sequence of pick-and-drop primitives driven by visual data. A decision making process learns the optimal grasping location exploiting deep Q-learning and finds the best releasing point from a path representation of the DLO shape. The system effectively combines a state-of-the-art algorithm for semantic segmentation specifically designed for DLOs with deep reinforcement learning. Experimental results show that our system is capable to manipulate a DLO into a variety of different shapes in few steps. The intermediate steps of deformation that lead the object from its initial configuration to the target one are also provided and analyzed.openZanella R.; Palli G.Zanella R.; Palli G

Deep Learning of Force Manifolds from the Simulated Physics of Robotic Paper Folding

Robotic manipulation of slender objects is challenging, especially when the induced deformations are large and nonlinear. Traditionally, learning-based control approaches, such as imitation learning, have been used to address deformable material manipulation. These approaches lack generality and often suffer critical failure from a simple switch of material, geometric, and/or environmental (e.g., friction) properties. This article tackles a fundamental but difficult deformable manipulation task: forming a predefined fold in paper with only a single manipulator. A data-driven framework combining physically-accurate simulation and machine learning is used to train a deep neural network capable of predicting the external forces induced on the manipulated paper given a grasp position. We frame the problem using scaling analysis, resulting in a control framework robust against material and geometric changes. Path planning is then carried out over the generated "neural force manifold" to produce robot manipulation trajectories optimized to prevent sliding, with offline trajectory generation finishing 15$\times$ faster than previous physics-based folding methods. The inference speed of the trained model enables the incorporation of real-time visual feedback to achieve closed-loop sensorimotor control. Real-world experiments demonstrate that our framework can greatly improve robotic manipulation performance compared to state-of-the-art folding strategies, even when manipulating paper objects of various materials and shapes.Comment: Supplementary video is available on YouTube: https://youtu.be/k0nexYGy-P

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

Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation

The robotic manipulation of Deformable Linear Objects (DLOs) is a vital and challenging task that is important in many practical applications. Classical model-based approaches to this problem require an accurate model to capture how robot motions affect the deformation of the DLO. Nowadays, data-driven models offer the best tradeoff between quality and computation time. This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method. Moreover, we introduce a data augmentation technique, which improves the prediction performance of almost all considered DLO data-driven models. Thanks to this technique, even a simple Multilayer Perceptron (MLP) achieves close to state-of-the-art performance while being significantly faster to evaluate. In the experiments, we compare the performance of the learning-based 3D models of the DLO on several challenging datasets quantitatively and demonstrate their applicability in the task of shaping a DLO.Comment: Under review for IEEE Robotics and Automation Letter

Spring Loaded Camming Device

Spring loaded camming devices or “cams” are used in traditional rock climbing as a means of active fall protection. Climbers place cams in cracks and fissures in the rock wall. The cam’s lobes press against the walls, locking it in place, anchoring the climber in case of a fall. Currently, there is a lack of large cams on the market. Only two small companies produce cams that are usable in cracks 6.5 inches wide and larger, however their designs are either too heavy and/or lack features to be comfortable. We are a group of mechanical engineering students at Cal Poly San Luis Obispo, and at the beginning of this project we aimed to design, manufacture, and test a large active fall protection device that improves on the currently available designs. Primarily, we wanted our design to be lightweight, strong, and have a semi-flexible stem. Due to the COVID-19 outbreak and campus closure in March 2020, we were forced to adapt and modify our goals to be achievable while we continued to work remotely. Since we did have access to Cal Poly facilities, we built a single camming device instead of the planned ten and were unable to tensile test the final cam. Even so, we feel that the testing results obtained from this prototype will be able to guide future iterations. The Final Design Report summarizes the background and market research we conducted, explains our objectives of the project, outlines and justifies the design concept, describes our final prototype and how we manufactured it, and details the formal testing procedure required to validate our calculations as well as provides recommendations for moving forward. We found that the prototype met all specifications but for the weight limit. It costs less than $130 per cam to manufacture, is usable in the targeted 6-9 inch range of rock crack widths, and has a flexible stem as requested by the climbing community. The final weight of the cam is 1135 g, which is a bit above our maximum desired weight of 900 g. We are confident that this design has the capability to take significant weight off the design with continued tensile testing