183 research outputs found
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
TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots
Tactile information is important for robust performance in robotic tasks that
involve physical interaction, such as object manipulation. However, with more
data included in the reasoning and control process, modeling behavior becomes
increasingly difficult. Deep Reinforcement Learning (DRL) produced promising
results for learning complex behavior in various domains, including
tactile-based manipulation in robotics. In this work, we present our
open-source reinforcement learning environments for the TIAGo service robot.
They produce tactile sensor measurements that resemble those of a real
sensorised gripper for TIAGo, encouraging research in transfer learning of DRL
policies. Lastly, we show preliminary training results of a learned force
control policy and compare it to a classical PI controller
Hybrid Planning: Task-Space Control and Sampling-Based Planning
Haschke R. Hybrid Planning: Task-Space Control and Sampling-Based Planning. In: Workshop on Robot Motion Planning: Online, Reactive, and in Real-time. 2012.We propose a hybrid approach to motion planning for redundant robots, which combines a powerful control framework with a sampling-based planner. We argue that a suitably chosen task controller already manages a huge amount of trajectory planning work. However, due to its local approach to obstacle avoidance, it may get stuck in local minima. Therefore we augment it with a globally acting planner, which operates in a lower-dimensional search space, thus circumventing the curse of dimensionality afflicting modern, many-DoF robots
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 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
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
Bio-Inspired Motion Strategies for a Bimanual Manipulation Task
Steffen JF, Elbrechter C, Haschke R, Ritter H. Bio-Inspired Motion Strategies for a Bimanual Manipulation Task. In: International Conference on Humanoid Robots (Humanoids). 2010
- …