1,762 research outputs found
Real-time Pipeline for Object Modeling and Grasping Pose Selection via Superquadric Functions
This work provides a novel real-time pipeline for modeling and grasping of unknown objects with a humanoid robot. Such a problem is of great interest for the robotic community, since conventional approaches fail when the shape, dimension, or pose of the objects are missing. Our approach reconstructs in real-time a model for the object under consideration and represents the robot hand both with proper and mathematically usable models, i.e., superquadric functions. The volume graspable by the hand is represented by an ellipsoid and is defined a priori, because the shape of the hand is known in advance. The superquadric representing the object is obtained in real-time from partial vision information instead, e.g., one stereo view of the object under consideration, and provides an approximated 3D full model. The optimization problem we formulate for the grasping pose computation is solved online by using the Ipopt software package and, thus, does not require off-line computation or learning. Even though our approach is for a generic humanoid robot, we developed a complete software architecture for executing this approach on the iCub humanoid robot. Together with that, we also provide a tutorial on how to use this framework. We believe that our work, together with the available code, is of a strong utility for the iCub community for three main reasons: object modeling and grasping are relevant problems for the robotic community, our code can be easily applied on every iCub, and the modular structure of our framework easily allows extensions and communications with external code
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
The importance of depth perception in the interactions that humans have
within their nearby space is a well established fact. Consequently, it is also
well known that the possibility of exploiting good stereo information would
ease and, in many cases, enable, a large variety of attentional and interactive
behaviors on humanoid robotic platforms. However, the difficulty of computing
real-time and robust binocular disparity maps from moving stereo cameras often
prevents from relying on this kind of cue to visually guide robots' attention
and actions in real-world scenarios. The contribution of this paper is
two-fold: first, we show that the Efficient Large-scale Stereo Matching
algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map
is well suited to be used on a humanoid robotic platform as the iCub robot;
second, we show how, provided with a fast and reliable stereo system,
implementing relatively challenging visual behaviors in natural settings can
require much less effort. As a case of study we consider the common situation
where the robot is asked to focus the attention on one object close in the
scene, showing how a simple but effective disparity-based segmentation solves
the problem in this case. Indeed this example paves the way to a variety of
other similar applications
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central
importance to have good knowledge of both end-effector, object pose and shape.
In this work we propose a framework for markerless visual servoing on unknown
objects, which is divided in four main parts: I) a least-squares minimization
problem is formulated to find the volume of the object graspable by the robot's
hand using its stereo vision; II) a recursive Bayesian filtering technique,
based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
(position and orientation) of the robot's end-effector without the use of
markers; III) a nonlinear constrained optimization problem is formulated to
compute the desired graspable pose about the object; IV) an image-based visual
servo control commands the robot's end-effector toward the desired pose. We
demonstrate effectiveness and robustness of our approach with extensive
experiments on the iCub humanoid robot platform, achieving real-time
computation, smooth trajectories and sub-pixel precisions
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