Service robots need many different informations about the objects, to grasp and manipulate them. Besides the physical information such as the geometry and weight, semantic information of the objects is also needed. To model both of these informations, we have constructed a multimodal object modeling center. It can model the physical properties of the object, such as the textures and the 3D geometry, with a digitizer and movable stereo cameras. Other properties of the objects relevant for grasping can also be automatically computed. Further, a human teacher can communicate with the system with multimodal techniques to bring the semantic information about the grasping to the system. The modeled information in this modeling center covers all the information a grasp planning needs. We have implemented a grasp planning system based on the grasp simulator “GraspIt!” to plan high quality grasps. The semantic information is represented as shape primitives, which are treated by the grasp planning as obstacles or must-touch regions of object to influence the resulted grasps. The modeled physical, semantic and automatically computed information, together with the computed grasps are saved in a database, which provides the service robot the needed knowledge to grasp household objects.

An automatic grasp planning system for service robots

This paper presents a knowledge model of everyday objects for semantic grasp. This model is intended for extracting the grasp areas of everyday objects and approach directions for grasping when the 3D point cloud data and the intended purpose are given. Parts that make up everyday objects have functions related to their manipulation. We therefore represent everyday objects in terms of connected parts of functional units. This knowledge model describes the structure of everyday objects and information on their manipulation. The structure of an everyday object describes component parts of the object in terms of simple shape primitives to provide geometrical information and describes connections between parts with kinematic attributes. The information on the structure is used to map the manipulation knowledge onto the 3D point cloud data. The manipulation knowledge of the object includes the grasp areas and approach directions for the intended purpose. Fine grasps suitable for the intended task can be generated by performing a grasp planning with consideration for stable grasp and the kinematics of the robot in the grasp areas and approach directions.

Modeling of everyday objects for semantic grasp

In this paper we present an overview of a multi-sensor setup designed to record and analyse human in-hand manipulation — tasks consisting of several phases of finger motions following the initial grasp. During the experiments all of the hand, finger, and object positions are recorded, as are the contact forces applied to the manipulated objects. The use of instrumented sensing objects complements the data. The goal is to understand and extract a basic set of finger and hand movement patterns, which can then be combined to perform a complete manipulation task, and which can be transferred to control robotic hands. The segmentation of whole manipulation traces into several phases corresponding to individual basic patterns is the first step towards this goal. Initial analysis and segmentation of two typical manipulation tasks are presented, showing the advantages of the multi-modal analysis.

/pdf/multi-sensor-based-segmentation-of-human-manipulation-tasks-4cgmfii290.pdf

Multi-sensor based segmentation of human manipulation tasks

Intelligent object manipulation is critical for a robot to effectively operate in a household environment. There are many grasp planners that can estimate grasps based on object shape, but these approaches often perform poorly because they miss key information about non-visual object characteristics. Object model databases can account for this information, but existing methods for database construction are time and resource intensive. We present an easy-to-use system for constructing a grasp database from crowdsourced demonstrations. The method requires no additional equipment other than the robot itself, and non-expert users can demonstrate grasps through an intuitive web interface, with virtually no training required. We show that the crowdsourced grasps can prove sufficient for object manipulation, and furthermore the demonstration approach outperforms purely vision-based grasp planning approaches for a wide variety of object classes.

Construction of an object manipulation database from grasp demonstrations.

Object handovers between humans are common in our daily life but the mechanisms underlying handovers are still largely unclear. A good understanding of these mechanisms is important not only for a better understanding of human social behaviors, but also for the prospect of an automatized society in which machines will need to perform similar objects exchanges with humans. In this paper, we analyzed how humans determine the location of object transfer during handovers- to determine whether they can predict the preferred handover location of a partner, the variation of this prediction in 3D space, and to examine how much of a role vision plays in the whole process. For this we developed a paradigm that allows us to compare handovers by humans with and without on-line visual feedback. Our results show that humans have the surprising ability to modulate their handover location according to partners they have just met such that the resulting handover errors are in the order of few centimeters, even in the absence of vision. The handover errors are least along the axis joining the two partners, suggesting a limited role for visual feedback in this direction. Finally, we show that the handover locations are explained very well by a linear model considering the heights, genders and social dominances of the two partners, and the distance between them. We developed separate models for the behavior of 'givers' and 'receivers' and discuss how the behavior of the same individual changes depending on his role in the handover.

https://hal-lirmm.ccsd.cnrs.fr/lirmm-02158921/document

The where of handovers by humans: Effect of partner characteristics, distance and visual feedback

In this paper a novel criterion for grasp evaluation is presented. Commonly used criteria for grasp evaluation take only forces into account. This way of describing a grasp is not intuitively usable by humans and does not consider the semantics of objects. The proposed criterion is based on the reusability of a grasped object. For example some grasps which might be form- or force-closure grasps may not be suitable with respect to a certain task, being executed after the object has been grasped. Our criterion closes this gap between force- stability and manageability. The reusability is defined via four typical tasks for a service robot scenario which represent some of the actions. It is more convenient for humans to use than the other force-based criteria, because the proposed actions are understandable by everybody and are not as abstract as force- based criteria. The actions themselves are defined by the forces occurring during operation performance and an additional keep- out-area if necessary.

Reusability-based Semantics for Grasp Evaluation in Context of Service Robotics

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Reusability-based Semantics for Grasp Evaluation in Context of Service Robotics