Acronym: A large-scale grasp dataset based on simulation
We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation.
The dataset contains 17.7 M parallel-jaw grasps, spanning 8872 objects from 262 different …
The dataset contains 17.7 M parallel-jaw grasps, spanning 8872 objects from 262 different …
Deep learning for detecting robotic grasps
We consider the problem of detecting robotic grasps in an RGB-D view of a scene
containing objects. In this work, we apply a deep learning approach to solve this problem …
containing objects. In this work, we apply a deep learning approach to solve this problem …
Interactive perception: Leveraging action in perception and perception in action
Recent approaches in robot perception follow the insight that perception is facilitated by
interaction with the environment. These approaches are subsumed under the term …
interaction with the environment. These approaches are subsumed under the term …
Efficient learning of goal-oriented push-grasping synergy in clutter
We focus on the task of goal-oriented grasping, in which a robot is supposed to grasp a pre-
assigned goal object in clutter and needs some pre-grasp actions such as pushes to enable …
assigned goal object in clutter and needs some pre-grasp actions such as pushes to enable …
Dipn: Deep interaction prediction network with application to clutter removal
We propose a Deep Interaction Prediction Network (DIPN) for learning to predict complex
interactions that ensue as a robot end-effector pushes multiple objects, whose physical …
interactions that ensue as a robot end-effector pushes multiple objects, whose physical …
Robotic objects detection and grasping in clutter based on cascaded deep convolutional neural network
D Liu, X Tao, L Yuan, Y Du… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The complex and changeable robotic operating environment will often cause the low
success rate or failure of the robot grasping. This article proposes a grasp pose detection …
success rate or failure of the robot grasping. This article proposes a grasp pose detection …
Randomized physics-based motion planning for grasping in cluttered and uncertain environments
Planning motions to grasp an object in cluttered and uncertain environments is a
challenging task, particularly when a collision-free trajectory does not exist and objects …
challenging task, particularly when a collision-free trajectory does not exist and objects …
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 …
Learning to manipulate unknown objects in clutter by reinforcement
We present a fully autonomous robotic system for grasping objects in dense clutter. The
objects are unknown and have arbitrary shapes. Therefore, we cannot rely on prior models …
objects are unknown and have arbitrary shapes. Therefore, we cannot rely on prior models …
Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations
For applications such as Amazon warehouse order fulfillment, robots must grasp a desired
object amid clutter: other objects that block direct access. This can be difficult to program …
object amid clutter: other objects that block direct access. This can be difficult to program …