Three-Dimensional Deformable Object Manipulation Using Fast Online Gaussian Process Regression

Hu, Zhe; Sun, Peigen; Pan, Jia

doi:10.1109/lra.2018.2793339

Cited by 73 publications

(50 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tasks such as pouring a specific volume of fluid in a container and cleaning a compliant part were successfully learned and generalized to handle different parameters. Hu et al [105] performed the online learning of an object's deformation model by using a Gaussian Process Regression algorithm that selectively ignores uninformative data. This learned model is used to build a visual servoing controller to manage the 3D deformation of various objects.…”

Section: Learned Controlmentioning

confidence: 99%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

View full text Add to dashboard Cite

This paper aims to provide a comprehensive survey of recent advancements in modelling and autonomous manipulation of non-rigid objects. It first summarizes the recent advances in sensing and modelling of such objects with a focus on describing the methods and technologies used to measure their shape and estimate their material and physical properties. Formal representations considered to predict the deformation resulting from manipulation of non-rigid objects are then investigated. The third part provides a survey of planning and control strategies exploited to operate dexterous robotic systems while performing various tasks on objects made of different non-rigid materials.

show abstract

Section: Learned Controlmentioning

confidence: 99%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

View full text Add to dashboard Cite

show abstract

“…For simplicity, our random-forest uses HOW-features as inputs. Another feature recently proposed in [21] represents cloth using a small set of feature points. However, these feature points can only characterize small-scale deformations because there can be a lot of occlusions under large deformations.…”

Section: Related Workmentioning

confidence: 99%

Cloth Manipulation Using Random-Forest-Based Imitation Learning

Jia

Pan

et al. 2019

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

We present a novel approach for manipulating high-DOF deformable objects such as cloth. Our approach uses a random-forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random-forest is determined automatically based on the training data, which consists of visual features and control signals. The training data is constructed online using an imitation learning algorithm. We have evaluated our approach on different cloth manipulation benchmarks such as flattening, folding, and twisting. In all these tasks, we have observed convergent behavior for the random-forest. On convergence, the random-forest-based controller exhibits superior robustness to observation noise compared with other techniques such as convolutional neural networks and nearest neighbor searches.

show abstract

“…The models depend greatly on the type of object; linear objects, for instance, admit representations suitable for shape planning [16], [17]. Other authors favor modelfree approaches [2], [18], in some cases using a deformation Jacobian estimated online from sensory data [19]- [21]. For feedback control, methods not reliant on prior models are considered more generalizable and computationally simpler.…”

Section: A Related Workmentioning

confidence: 99%

Deformation-based shape control with a multirobot system

Aranda

Ramón

Mezouar

2019

2019 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

We present a novel method to control the relative positions of the members of a robotic team. The application scenario we consider is the cooperative manipulation of a deformable object in 2D space. A typical goal in this kind of scenario is to minimize the deformation of the object with respect to a desired state. Our contribution, then, is to use a global measure of deformation directly in the feedback loop. In particular, the robot motions are based on the descent along the gradient of a metric that expresses the difference between the team's current configuration and its desired shape. Crucially, the resulting multirobot controller has a simple expression and is inexpensive to compute, and the approach lends itself to analysis of both the transient and asymptotic dynamics of the system. This analysis reveals a number of properties that are interesting for a manipulation task: fundamental geometric parameters of the team (size, orientation, centroid, and distances between robots) can be suitably steered or bounded. We describe different policies within the proposed deformationbased control framework that produce useful team behaviors. We illustrate the methodology with computer simulations.

show abstract

Three-Dimensional Deformable Object Manipulation Using Fast Online Gaussian Process Regression

Cited by 73 publications

References 24 publications

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

Cloth Manipulation Using Random-Forest-Based Imitation Learning

Deformation-based shape control with a multirobot system

Contact Info

Product

Resources

About