Fast and Flexible Multi-Step Cloth Manipulation Planning Using an Encode-Manipulate-Decode Network (EM*D Net)

We propose a deep neural network architecture, the Encode-Manipulate-Decode (EM*D) net, for rapid manipulation planning on deformable objects. We demonstrate its effectiveness on simulated cloth. The net consists of 3D convolutional encoder and decoder modules that map cloth states to and from latent space, with a “manipulation module” in between that learns a forward model of the cloth's dynamics w.r.t. the manipulation repertoire, in latent space. The manipulation module's architecture is specialized for its role as a forward model, iteratively modifying a state representation by means of residual connections and repeated input at every layer. We train the network to predict the post-manipulation cloth state from a pre-manipulation cloth state and a manipulation input. By training the network end-to-end, we force the encoder and decoder modules to learn a latent state representation that facilitates modification by the manipulation module. We show that the network can achieve good generalization from a training dataset of 6,000 manipulation examples. Comparative experiments without the architectural specializations of the manipulation module show reduced performance, confirming the benefits of our architecture. Manipulation plans are generated by performing error back-propagation w.r.t. the manipulation inputs. Recurrent use of the manipulation network during planning allows for generation of multi-step plans. We show results for plans of up to three manipulations, demonstrating generally good approximation of the goal state. Plan generation takes <2.5 s for a three-step plan and is found to be robust to cloth self-occlusion, supporting the approach' viability for practical application

Clothing classification using image features derived from clothing fabrics, wrinkles and cloth overlaps

Abstract-This paper describes about a method of clothing classification using a single image. The method assumes to be used for building autonomous systems, with the purpose of recognizing day-to-day clothing thrown casually. A set of Gabor filters is applied to an input image, and then several image features that are invariant to translation, rotation and scale are generated. In this paper, we propose the descriptions of the features with focusing on clothing fabrics, wrinkles and cloth overlaps. Experiments of state description and classification using real clothing show the effectiveness of the proposed method

An experimental study on surface state description by wiping motion for the estimation of floor surface condition using indoor search robot

Abstract In this paper, we aimed to establish a novel method for surface condition measurement for the indoor floor. To measure the surface condition, we proposed wiping motion that to stroke the target surface with changing the stroking speed. We developed the wiping device with a 6-axis force sensor, a passive pivot, and a contact plate to realize the wiping motion. In the experiment, the surface condition was measured using four kinds of floor materials and two kinds of liquids. From the experimental results, it was confirmed that the resistance force depends on the wiping velocity. From the experimental results, we confirmed the effectiveness of the proposed method and examined the quantitative index used for surface state description

Feasibility Study of Textureless Object Detection and Pose Estimation Based on a Model with 3D Edgels and Surfaces

This paper describes a method for the detection of textureless objects. Our target objects include furniture and home appliances, which have no rich textural features or characteristic shapes. Focusing on the ease of application, we define a model that represents objects in terms of three-dimensional edgels and surfaces. Object detection is performed by superimposing input data on the model. A two-stage algorithm is applied to bring out object poses. Surfaces are used to extract candidates fromthe input data, and edgels are then used to identify the pose of a target object using two-dimensional template matching. Experiments using four real furniture and home appliances were performed to show the feasibility of the proposed method.We suggest the possible applicability in occlusion and clutter conditions