Industrial RobotFrom robotic hands to human hands: a visualization and simulation engine for grasping research - Miller, Allen, et al. - 2005A. Miller, P. Allen, V. Santos, and F. Valero-Cuevas, "From robotic hands to human hands: A visualization and simulation engine for grasping research...
A challenge in robot grasping is to achieve task-grasping which is to select a grasp that is advantageous to the success of tasks before and after grasps. One of the frameworks to address this difficulty is Learning-from-Observation (LfO), which obtains various hints from human demonstrations....
Based on this strategy, we show in this paper that learning from human experiences is a way to accomplish our goal of robot grasp synthesis for unknown objects. In this article we address an artificial system that relies on knowledge from previous human object grasping demonstrations. A learning...
A challenge in robot grasping is to achieve task-grasping which is to select a grasp that is advantageous to the success of tasks before and after grasps. One of the frameworks to address this difficulty is Learning-from-Observation (LfO), which obtains various hints from human demonstrations....
Deals with the programming of robots to perform grasping tasks. To do this, the assembly plan from observation (APO) paradigm is adopted, where the key idea is to enable a system to observe a human performing a grasping task, understand it, and perform the task with minimal human interventio...
to the building quality of the robot, reduced stiffness of joints in case of continuous operation, a quite limited grasping ability of Nao's hands with their pincer-shaped three fingers, as well as severe self-occlusion when the hand approaches the object in its final grasping position (the ...
et al. Design and implementation of an anthropomorphic hand for replicating human grasping functions. IEEE Trans. Robot. 32, 652–671 (2016). Article Google Scholar Duan, S. S. et al. Waterproof mechanically robust multifunctional conformal sensors for underwater interactive human–machine ...
Current model free learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human...
This helps give a robot the senses of touch and vision by analysing what it is grasping with its robotic arms or seeing through its camera. In particular, for the sense of touch, the NUS team applied an artificial skin that can detect touches more than 1,000...
(VR) enables robot learning from demonstration in a virtual environment. In this environment, a human user can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large ...