text: We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions. Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task. The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot. The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches. The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
  @cite_85: 
  @cite_51: The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects, but still remains an open problem. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects. Moreover, possible configurations also depend on the specific kinematics of the robotic arm and hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time.
  @cite_23: We design an example based planning framework to generate semantic grasps, stable grasps that are functionally suitable for specific object manipulation tasks. We propose to use partial object geometry, tactile contacts, and hand kinematic data as proxies to encode semantic constraints, which are task-related constraints. We introduce a semantic affordance map, which relates local geometry to a set of predefined semantic grasps that are appropriate to different tasks. Using this map, the pose of a robotic hand can be estimated so that the hand is adjusted to achieve the ideal approach direction required by a particular task. A grasp planner is then used to generate a set of final grasps which have appropriate stability, tactile contacts, and hand kinematics along this approach direction. We show experiments planning semantic grasps on everyday objects and executing these grasps with a physical robot.
  @cite_49: Robust vision-based grasping is still a hard problem for humanoid robot systems. When being restricted to using the camera system built-in into the robot's head for object localization, the scenarios get often very simplified in order to allow the robot to grasp autonomously. Within the computer vision community, many object recognition and localization systems exist, but in general, they are not tailored to the application on a humanoid robot. In particular, accurate 6D object localization in the camera coordinate system with respect to a 3D rigid model is crucial for a general framework for grasping. While many approaches try to avoid the use of stereo calibration, we will present a system that makes explicit use of the stereo camera system in order to achieve maximum depth accuracy. Our system can deal with textured objects as well as objects that can be segmented globally and are defined by their shape. Thus, it covers the cases of objects with complex texture and complex shape. Our work is directly linked to a grasping framework being implemented on the humanoid robot ARM AR and serves as its perception module for various grasping and manipulation experiments in a kitchen scenario.
summary: A slightly less independent'' procedure is to match and apply grasping templates to the sensed input @cite_51 . For graping based on existing knowledge-bases, one option is to detect known objects followed by executing a specific grasp linked to that particular instance or that category of objects @cite_23 @cite_49 . In @cite_85 , shape primitives are linked to the target object and used to seed appropriate contact-level grasping.