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Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
Gras** unseen objects in unconstrained, cluttered environments is an essential skill for
autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning …
autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning …
Unigrasp: Learning a unified model to grasp with multifingered robotic hands
To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a
role as important as the object geometry. The majority of previous work has focused on …
role as important as the object geometry. The majority of previous work has focused on …
Affordance detection for task-specific gras** using deep learning
In this paper we utilize the notion of affordances to model relations between task, object and
a grasp to address the problem of task-specific robotic gras**. We use convolutional …
a grasp to address the problem of task-specific robotic gras**. We use convolutional …
Generating multi-fingered robotic grasps via deep learning
This paper presents a deep learning architecture for detecting the palm and fingertip
positions of stable grasps directly from partial object views. The architecture is trained using …
positions of stable grasps directly from partial object views. The architecture is trained using …
Hierarchical fingertip space: A unified framework for grasp planning and in-hand grasp adaptation
We present a unified framework for grasp planning and in-hand grasp adaptation using
visual, tactile, and proprioceptive feedback. The main objective of the proposed framework is …
visual, tactile, and proprioceptive feedback. The main objective of the proposed framework is …
Learning of grasp adaptation through experience and tactile sensing
To perform robust gras**, a multi-fingered robotic hand should be able to adapt its
gras** configuration, ie, how the object is grasped, to maintain the stability of the grasp …
gras** configuration, ie, how the object is grasped, to maintain the stability of the grasp …
Planning grasps with suction cups and parallel grippers using superimposed segmentation of object meshes
This article develops model-based grasp planning algorithms. It focuses on industrial end-
effectors like grippers and suction cups, and plans grasp configurations considering …
effectors like grippers and suction cups, and plans grasp configurations considering …
End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer
Nonprehensile rearrangement is the problem of controlling a robot to interact with objects
through pushing actions in order to reconfigure the objects into a predefined goal pose. In …
through pushing actions in order to reconfigure the objects into a predefined goal pose. In …
Rearrangement with nonprehensile manipulation using deep reinforcement learning
Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task
which requires skillful interaction with the physical world. Usually, this is achieved by …
which requires skillful interaction with the physical world. Usually, this is achieved by …
Hybrid physical metric for 6-dof grasp pose detection
6-DoF grasp pose detection of multi-grasp and multi-object is a challenge task in the field of
intelligent robot. To imitate human reasoning ability for gras** objects, data driven …
intelligent robot. To imitate human reasoning ability for gras** objects, data driven …