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Achieving both high speed and precision in robot operations is a significant challenge for social implementation. While factory robots excel at predefined tasks, they struggle with environment-specific actions like cleaning and cooking.…

机器人学 · 计算机科学 2024-08-21 Masaki Yoshikawa , Hiroshi Ito , Tetsuya Ogata

Efficiently generating grasp poses tailored to specific regions of an object is vital for various robotic manipulation tasks, especially in a dual-arm setup. This scenario presents a significant challenge due to the complex geometries…

Manipulation tasks are sequential in nature. Grasp selection approaches that take into account the con- straints at each task step are critical, since they allow to both (1) Identify grasps that likely require simple arm motions through the…

机器人学 · 计算机科学 2017-09-05 Ana C. Huamán Quispe

In this article, we study the problem of selecting a grasping pose on the surface of an object to be manipulated by considering three post-grasp objectives. These objectives include (i) kinematic manipulation capability, (ii) torque effort…

机器人学 · 计算机科学 2017-12-13 Amir M Ghalamzan E , Nikos Mavrakis , Rustam Stolkin

Perception-for-grasping is a challenging problem in robotics. Inexpensive range sensors such as the Microsoft Kinect provide sensing capabilities that have given new life to the effort of developing robust and accurate perception methods…

机器人学 · 计算机科学 2013-11-14 Andreas ten Pas , Robert Platt

Performing a grasp is a pivotal capability for a robotic gripper. We propose a new evaluation approach of grasping stability via constructing a model of grasping stiffness based on the theory of contact mechanics. First, the mathematical…

机器人学 · 计算机科学 2018-10-22 Huixu Dong , Chen Qiu , Dilip K. Prasad , Ye Pan , Jiansheng Dai , I-Ming Chen

Grasping objects with diverse mechanical properties, such as heavy, slippery, or fragile items, remains a significant challenge in robotics. Conventional grippers often rely on applying high normal forces, which can cause damage to objects.…

Robot pick and place systems have traditionally decoupled grasp, placement, and motion planning to build sequential optimization pipelines with the assumption that the individual components will be able to work together. However, this…

机器人学 · 计算机科学 2025-07-25 Benjamin H. Leebron , Kejia Ren , Yiting Chen , Kaiyu Hang

In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick and place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This…

机器人学 · 计算机科学 2020-06-22 Fahad Islam , Oren Salzman , Aditya Agarwal , Maxim Likhachev

It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments. Towards building scalable systems that can perform diverse manipulation tasks over various 3D…

计算机视觉与模式识别 · 计算机科学 2023-03-28 Yan Zhao , Ruihai Wu , Zhehuan Chen , Yourong Zhang , Qingnan Fan , Kaichun Mo , Hao Dong

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 obstructing the way are required to be carefully grasped and moved…

机器人学 · 计算机科学 2017-11-28 Muhayyuddin , Mark Moll , Lydia Kavraki , Jan Rosell

In this paper we explore state-of-the-art underactuated, compliant robot gripper designs through looking at their performance on a generic grasping task. Starting from a state of the art open gripper design, we propose design…

机器人学 · 计算机科学 2016-01-19 Eduardo Ruiz , Walterio Mayol-Cuevas

This paper presents the application of a learning control approach for the realization of a fast and reliable pick-and-place application with a spherical soft robotic arm. The arm is characterized by a lightweight design and exhibits…

机器人学 · 计算机科学 2021-03-09 Jasan Zughaibi , Matthias Hofer , Raffaello D'Andrea

Deep learning has been widely used for inferring robust grasps. Although human-labeled RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of large dataset is expensive. To address this problem, images…

Grasping the same object in different postures is often necessary, especially when handling tools or stacked items. Due to unknown object properties and changes in grasping posture, the required grasping force is uncertain and variable.…

机器人学 · 计算机科学 2025-03-17 Qiyin Huang , Ruomin Sui , Lunwei Zhang , Yenhang Zhou , Tiemin Li , Yao Jiang

Robotic grasping presents a difficult motor task in real-world scenarios, constituting a major hurdle to the deployment of capable robots across various industries. Notably, the scarcity of data makes grasping particularly challenging for…

机器人学 · 计算机科学 2024-06-18 Abhi Kamboj , Katherine Driggs-Campbell

We consider the problem of reorienting a rigid object with arbitrary known shape on a table using a two-finger pinch gripper. Reorienting problem is challenging because of its non-smoothness and high dimensionality. In this work, we focus…

机器人学 · 计算机科学 2019-12-06 Yifan Hou , Zhenzhong Jia , Matthew T. Mason

Soft grippers are receiving growing attention due to their compliance-based interactive safety and dexterity. Hybrid gripper (soft actuators enhanced by rigid constraints) is a new trend in soft gripper design. With right structural…

机器人学 · 计算机科学 2021-10-20 Wenpei Zhu , Chenghua Lu , Qule Zheng , Zhonggui Fang , Haichuan Che , Kailuan Tang , Mingchao Zhu , Sicong Liu , Zheng Wang

This paper studies the time-optimal path tracking problem for a team of cooperating robotic manipulators carrying an object. Considering the problem for rigidly grasped objects, we show that it can be cast as a convex optimization problem…

机器人学 · 计算机科学 2023-03-14 Hamed Haghshenas , Anders Hansson , Mikael Norrlöf

Achieving generalizable and precise robotic manipulation across diverse environments remains a critical challenge, largely due to limitations in spatial perception. While prior imitation-learning approaches have made progress, their…

机器人学 · 计算机科学 2025-05-28 Yiqi Huang , Travis Davies , Jiahuan Yan , Jiankai Sun , Xiang Chen , Luhui Hu