Related papers: A Grasping-centered Analysis for Cloth Manipulatio…
The development of tactile sensing and its fusion with computer vision is expected to enhance robotic systems in handling complex tasks like deformable object manipulation. However, readily available industrial grippers typically lack…
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…
Robotic manipulation with two-finger grippers is challenged by objects lacking distinct graspable features. Traditional pre-grasping methods, which typically involve repositioning objects or utilizing external aids like table edges, are…
Quadruped robots are increasingly used in various applications due to their high mobility and ability to operate in diverse terrains. However, most available quadruped robots are primarily focused on mobility without object manipulation…
Separating thin, flexible layers that must be individually grasped is a common but challenging manipulation primitive for most off-the-shelf grippers. A prominent example arises in clinical settings: the opening of sterile flat pouches for…
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…
Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framework for grasping with formal safety guarantees based on…
Data gloves play a crucial role in study of human grasping, and could provide insights into grasp synergies. Grasp synergies lead to identification of underlying patterns to develop control strategies for hand exoskeletons. This paper…
Humans grasp unfamiliar objects by combining an initial visual estimate with tactile and proprioceptive feedback during interaction. We present ShapeGrasp, a robotic implementation of this approach. The proposed method is an iterative…
Grasping is an essential capability for most robots in practical applications. Soft robotic grippers are considered as a critical part of robotic grasping and have attracted considerable attention in terms of the advantages of the high…
Teleoperation is often limited by the ability of an operator to react and predict the behavior of the robot as it interacts with the environment. For example, to grasp small objects on a table, the teleoperator needs to predict the position…
Many objects commonly found in household and industrial environments are represented by cylindrical and cubic shapes. Thus, it is available for robots to manipulate them through the real-time detection of elliptic and rectangle shape…
Deep learning is an established framework for learning hierarchical data representations. While compute power is in abundance, one of the main challenges in applying this framework to robotic grasping has been obtaining the amount of data…
Clothes manipulation, such as folding or hanging, is a critical capability for home service robots. Despite recent advances, most existing methods remain limited to specific clothes types and tasks, due to the complex, high-dimensional…
The rise in additive manufacturing comes with unique opportunities and challenges. Massive part customization and rapid design changes are made possible with additive manufacturing, however, manufacturing industries that desire the…
Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection…
Robotic grasping is a fundamental aspect of robot functionality, defining how robots interact with objects. Despite substantial progress, its generalizability to counter-intuitive or long-tailed scenarios, such as objects with uncommon…
We describe a force-controlled robotic gripper with built-in tactile and 3D perception. We also describe a complete autonomous manipulation pipeline consisting of object detection, segmentation, point cloud processing, force-controlled…
Learning-based approaches to grasp planning are preferred over analytical methods due to their ability to better generalize to new, partially observed objects. However, data collection remains one of the biggest bottlenecks for grasp…
This paper presents a hierarchical motion planner for planning the manipulation motion to repose long and heavy objects considering external support surfaces. The planner includes a task level layer and a motion level layer. We formulate…