Related papers: CloudGripper: An Open Source Cloud Robotics Testbe…
In this project, we focus on human-robot interaction in caregiving scenarios like bathing, where physical contact is inevitable and necessary for proper task execution because force must be applied to the skin. Using finite element…
Imitation learning and world models have shown significant promise in advancing generalizable robotic learning, with robotic grasping remaining a critical challenge for achieving precise manipulation. Existing methods often rely heavily on…
Recently, suction-based robotic systems with microscopic features or active suction components have been proposed to grip rough and irregular surfaces. However, sophisticated fabrication methods or complex control systems are required for…
This paper presents an aerially deployable crawler designed for adaptive locomotion and manipulation within tree canopies. The system combines compliant microspine-based tracks, a dual-track rotary gripper, and an elastic tail, enabling…
Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open…
Gathering real-world data from the robot quickly becomes a bottleneck when constructing a robot learning system for grasping. In this work, we design a semi-supervised grasping system that, on top of a small sample of robot experience,…
Multi-degree of freedom robots are playing very important role in different applications of automation. They are providing much more accuracy in carrying out a typical procedure as compared to the manual work done by human. In recent years…
Robotic grasping is a cornerstone capability of embodied systems. Many methods directly output grasps from partial information without modeling the geometry of the scene, leading to suboptimal motion and even collisions. To address these…
Robot grasping is an actively studied area in robotics, mainly focusing on the quality of generated grasps for object manipulation. However, despite advancements, these methods do not consider the human-robot collaboration settings where…
Robotic grasping, the ability of robots to reliably secure and manipulate objects of varying shapes, sizes and orientations, is a complex task that requires precise perception and control. Deep neural networks have shown remarkable success…
Robotics hand/grippers nowadays are not limited to manufacturing lines; instead, they are widely utilized in cluttered environments, such as restaurants, farms, and warehouses. In such scenarios, they need to deal with high uncertainty of…
This work proposes a novel generative design tool for passive grippers -- robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive…
We introduce Kleinkram, a free and open-source system designed to solve the challenge of managing massive, unstructured robotic datasets. Designed as a modular, on-premises cloud solution, Kleinkram enables scalable storage, indexing, and…
Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shape and surface properties remains, however, challenging. Most current…
Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects.…
This paper presents BURG-Toolkit, a set of open-source tools for Benchmarking and Understanding Robotic Grasping. Our tools allow researchers to: (1) create virtual scenes for generating training data and performing grasping in simulation;…
This paper introduces a novel robotic gripper, named as the SPD gripper. It features a palm and two mechanically identical and symmetrically arranged fingers, which can be driven independently or by a single motor. The fingertips of the…
Given the diversity of devices and the product upgrades, cross-device research has become an urgent issue that needs to be tackled. To this end, we pioneer in probing the cross-device (cameras & robotics) grasping policy in the 3D open…
We present a benchmarking study of vision-based robotic grasping algorithms with distinct approaches, and provide a comparative analysis. In particular, we compare two machine-learning-based and two analytical algorithms using an existing…
This paper introduces a novel, small form-factor, aerial vehicle research platform for agile object detection, classification, tracking, and interaction tasks. General-purpose hardware components were designed to augment a given aerial…