Efficient motion planning for manipulation robots in environments with deformable objects

Frank, Barbara; Stachniss, Cyrill; Abdo, Nichola; Burgard, Wolfram

doi:10.1109/iros.2011.6094946

Cited by 34 publications

(13 citation statements)

References 20 publications

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“…Deformable soft object manipulations have been attempted via classical methods such as motion planning and manipulation planning [29,13,19,17,41]. These approaches require manual engineering for object models.…”

Section: Related Workmentioning

confidence: 99%

Learning Robotic Manipulation through Visual Planning and Acting

Wang¹,

Kurutach²,

Liu³

et al. 2019

Robotics: Science and Systems XV

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Planning for robotic manipulation requires reasoning about the changes a robot can affect on objects. When such interactions can be modelled analytically, as in domains with rigid objects, efficient planning algorithms exist. However, in both domestic and industrial domains, the objects of interest can be soft, or deformable, and hard to model analytically. For such cases, we posit that a data-driven modelling approach is more suitable. In recent years, progress in deep generative models has produced methods that learn to 'imagine' plausible images from data. Building on the recent Causal InfoGAN generative model, in this work we learn to imagine goal-directed object manipulation directly from raw image data of self-supervised interaction of the robot with the object. After learning, given a goal observation of the system, our model can generate an imagined plan -a sequence of images that transition the object into the desired goal. To execute the plan, we use it as a reference trajectory to track with a visual servoing controller, which we also learn from the data as an inverse dynamics model. In a simulated manipulation task, we show that separating the problem into visual planning and visual tracking control is more sample efficient and more interpretable than alternative datadriven approaches. We further demonstrate our approach on learning to imagine and execute in 3 environments, the final of which is deformable rope manipulation on a PR2 robot.

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Section: Related Workmentioning

confidence: 99%

Learning Robotic Manipulation through Visual Planning and Acting

Wang¹,

Kurutach²,

Liu³

et al. 2019

Robotics: Science and Systems XV

View full text Add to dashboard Cite

show abstract

“…Large Deformation and Manipulation: Different techniques have been proposed for motion planning for deformable objects. Most of these works (e.g., [4,8,9]) focus on volumetric objects such as a deforming ball or linear deformable objects such as steerable needles. By comparison, cloth-like thinshell objects tend to exhibit more complex deformations, forming wrinkles and folds.…”

Section: Related Workmentioning

confidence: 99%

“…We evaluate the performance of our algorithm along with the others. The test involves measuring the residual of the manipulator as it moves towards the goal configuration based on the computed control parameters, as given by Equation 8.…”

Section: G Benefits Of Random-forestmentioning

confidence: 99%

Cloth Manipulation Using Random-Forest-Based Imitation Learning

Jia

Pan

et al. 2019

IEEE Robot. Autom. Lett.

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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.

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“…To avoid careful scanning, we propose a method which allows recovering information about the object from a single view. If a grasping [1] or motion planning algorithm [3] algorithm needs specific information about the object's visual representation, the recovered images can be used to provide such data.…”

Section: Introductionmentioning

confidence: 99%

Generate What You Can’t See - a View-dependent Image Generation

Piaskowski

Staszak

Belter

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In order to operate autonomously, a robot should explore the environment and build a model of each of the surrounding objects. A common approach is to carefully scan the whole workspace. This is time-consuming. It is also often impossible to reach all the viewpoints required to acquire full knowledge about the environment. Humans can perform shape completion of occluded objects by relying on past experience. Therefore, we propose a method that generates images of an object from various viewpoints using a single input RGB image. A deep neural network is trained to imagine the object appearance from many viewpoints. We present the whole pipeline, which takes a single RGB image as input and returns a sequence of RGB and depth images of the object. The method utilizes a CNN-based object detector to extract the object from the natural scene. Then, the proposed network generates a set of RGB and depth images. We show the results both on a synthetic dataset and on real images.

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Efficient motion planning for manipulation robots in environments with deformable objects

Cited by 34 publications

References 20 publications

Learning Robotic Manipulation through Visual Planning and Acting

Learning Robotic Manipulation through Visual Planning and Acting

Cloth Manipulation Using Random-Forest-Based Imitation Learning

Generate What You Can’t See - a View-dependent Image Generation

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