5 accepted papers
In this work, we describe a multi-object grasping benchmark to evaluate the grasping and manipulation capabilities of robotic systems in both pile and surface scenarios. The benchmark introduces three robot multi-object grasping benchmarking protocols designed to challenge different aspects of robot
Choosing a good grasp is fundamental for accomplishing robotic grasping and manipulation tasks. Typically, the grasp synthesis is addressed separately from the planning phase, which can lead to failures during the execution of the task. In addition, most of the current grasping approaches privilege
Grasping a variety of objects is still an open problem in robotics, especially for cluttered scenarios. Multimodal grasping has been recognized as a promising strategy to improve the manipulation capabilities of a robotic system. This work presents a novel grasp planning algorithm for hybrid gripper
The proposed work presents a framework based on Graph Neural Networks (GNN) that abstracts the task to be executed and directly allows the robot to learn task-specific rules from synthetic demonstrations given through imitation learning. A graph representation of the state space is considered to enc
Object detection and pose estimation are strict requirements for many robotic grasping and manipulation applications to endow robots with the ability to grasp objects with different properties in cluttered scenes and with various lighting conditions. This work proposes the framework <italic xmlns:mm