Development of an intelligent object for grasp and manipulation research

Kõiva R, Haschke R, Ritter H. Development of an intelligent object for grasp and manipulation research. Presented at the ICAR 2011, Tallinn, Estonia.In this paper we introduce a novel device, called iObject, which is equipped with tactile and motion tracking sensors that allow for the evaluation of human and robot grasping and manipulation actions. Contact location and contact force, object acceleration in space (6D) and orientation relative to the earth (3D magnetometer) are measured and transmitted wirelessly over a Bluetooth connection. By allowing human-human, human-robot and robot-robot comparisons to be made, iObject is a versatile tool for studying manual interaction. To demonstrate the efficiency and flexibility of iObject for the study of bimanual interactions, we report on a physiological experiment and evaluate the main parameters of the considered dual-handed manipulation task

Two-fingered, tactile-based manipulation of unknown objects

Li Q, Haschke R, Ritter H. Two-fingered, tactile-based manipulation of unknown objects. Presented at the RSS2013-WS: Sensitive Robotics, Berlin, Germany

Perceptual Grouping through Competition in Coupled Oscillator Networks

Meier M, Haschke R, Ritter H. Perceptual Grouping through Competition in Coupled Oscillator Networks. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). Bruges (Belgium): d-side; 2013.In this paper we present a novel approach to model perceptual grouping based on phase and frequency synchronization in a network of coupled Kuramoto oscillators. Transferring the grouping concept from the Competitive Layer Model (CLM) to a network of Kuramoto oscillators, we preserve the excellent grouping capabilities of the CLM, while dramatically improving the convergence rate, robustness to noise, and computational performance, which is verified in a series of artificial grouping experiments

Grasp Point Optimization for Unknown Object Manipulation in Hand Task

Li Q, Haschke R, Bolder B, Ritter H. Grasp Point Optimization for Unknown Object Manipulation in Hand Task. Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems, Portugal

Hierarchical Bayesian Modeling of Manipulation Sequences from Bimodal Input

Barchunova A, Moringen J, Haschke R, Ritter H. Hierarchical Bayesian Modeling of Manipulation Sequences from Bimodal Input. Presented at the Proceedings of the 11th International Conference on Cognitive Modeling, Berlin

Grasp Point Optimization by Online Exploration of Unknown Object Surface

Li Q, Haschke R, Bolder B, Ritter H. Grasp Point Optimization by Online Exploration of Unknown Object Surface. Presented at the IEEE-RAS International Conference on Humanoid Robots, Osaka

Deep Learning for Action Recognition in Augmented Reality Assistance Systems

Schröder M, Ritter H. Deep Learning for Action Recognition in Augmented Reality Assistance Systems. In: ACM SIGGRAPH 2017 Posters. 2017: 75:1-75:2

Hand-Object Interaction Detection with Fully Convolutional Networks

Schröder M, Ritter H. Hand-Object Interaction Detection with Fully Convolutional Networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 2017: 18-25.Detecting hand-object interactions is a challenging problem with many applications in the human-computer interaction domain. We present a real-time method that automatically detects hand-object interactions in RGBD sensor data and tracks the object’s rigid pose over time. The detection is performed using a fully convolutional neural network, which is purposefully trained to discern the relationship between hands and objects and which predicts pixel-wise class probabilities. This output is used in a probabilistic pixel labeling strategy that explicitly accounts for the uncertainty of the prediction. Based on the labeling of object pixels, the object is tracked over time using modelbased registration. We evaluate the accuracy and generalizability of our approach and make our annotated RGBD dataset as well as our trained models publicly available