Grasping for cues: Visual cue integration for object manipulation

Integration of Visual Cues for Robotic Grasping

In this paper, we propose a method that generates grasping actions for novel objects based on visual input from a stereo camera. We are integrating two methods that are advantageous either in predicting how to grasp an object or where to apply a grasp. The first one reconstructs a wire frame object model through curve matching. Elementary grasping actions can be associated to parts of this mode...

Object Manipulation from Simplified Visual Cues

Humanoid robotics in general, and human–robot interaction in particular, is gaining new, extensive fields of application, as it gradually becomes pervasive in our daily life. One of the actions that humanoid robots must perform is the manipulation of things (reaching their arms for objects, grasping and moving them). However, in order to do this, a robot must first have acquired some knowledge ...

Biologically Motivated Audio-Visual Cue Integration for Object Categorization

Auditory and visual cues are important sensor inputs for biological and artificial systems. They provide crucial information for navigating environments, recognizing categories, animals and people. How to combine effectively these two sensory channels is still an open issue. As a step towards this goal, this paper presents a comparison between three different multi-modal integration strategies,...

Cue integration for visual servoing

The robustness and reliability of vision algorithms is, nowadays, the key issue in robotic research and industrial applications. To control a robot in a closed-loop fashion, different tracking systems have been reported in the literature. A common approach to increased robustness of a tracking system is the use of different models (CAD model of the object, motion model) known a priori. Our hypo...

Capturing Human Hand Kinematics for Object Grasping and Manipulation