Related papers: Generalized Anthropomorphic Functional Grasping wi…
Handovers are basic yet sophisticated motor tasks performed seamlessly by humans. They are among the most common activities in our daily lives and social environments. This makes mastering the art of handovers critical for a social and…
Physical human-robot interactions (pHRI) are less efficient and communicative than human-human interactions, and a key reason is a lack of informative sense of touch in robotic systems. Interpreting human touch gestures is a nuanced,…
Learning robotic grasps from visual observations is a promising yet challenging task. Recent research shows its great potential by preparing and learning from large-scale synthetic datasets. For the popular, 6 degree-of-freedom (6-DOF)…
As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous across spatial and temporal domains. Few existing methods cover…
Grasping an unknown object is difficult for robot hands. When the characteristics of the object are unknown, knowing how to plan the speed at and width to which the fingers are narrowed is difficult. In this paper, we propose a method to…
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…
Application of soft and compliant joints in grasping mechanisms received an increasing attention during recent years. This article suggests the design and development of a novel bio-inspired compliant finger which is composed of a 3D…
We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are…
Humans, this species expert in grasp detection, can grasp objects by taking into account hand-object positioning information. This work proposes a method to enable a robot manipulator to learn the same, grasping objects in the most optimal…
Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery, and household automation. However, developing grasp…
The human hand plays a vital role in daily life and industrial applications, yet replicating its multifunctional capabilities-including motion, sensing, and coordinated manipulation with robotic systems remains a formidable challenge.…
Generalizable grasping with high-degree-of-freedom (DoF) dexterous hands remains challenging in tiered workspaces, where occlusion, narrow clearances, and height-dependent constraints are substantially stronger than in open tabletop scenes.…
Hand-object interaction(HOI) is the fundamental link between human and environment, yet its dexterous and complex pose significantly challenges for gesture control. Despite significant advances in AI and robotics, enabling machines to…
Replicating human--level dexterity remains a fundamental robotics challenge, requiring integrated solutions from mechatronic design to the control of high degree--of--freedom (DoF) robotic hands. While imitation learning shows promise in…
This paper introduces a framework to plan grasps with multi-fingered hands. The framework includes a multi-dimensional iterative surface fitting (MDISF) for grasp planning and a grasp trajectory optimization (GTO) for grasp imagination. The…
Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the…
Robotic grasping is a fundamental ability for a robot to interact with the environment. Current methods focus on how to obtain a stable and reliable grasping pose in object level, while little work has been studied on part (shape)-wise…
In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics…
This technical report gives an overview of our work on control algorithms dealing with redundant robot systems for achieving human-like motion characteristics. Previously, we developed a novel control law to exhibit human-motion…
Achieving precise and generalizable grasping across diverse objects and environments is essential for intelligent and collaborative robotic systems. However, existing approaches often struggle with ambiguous affordance reasoning and limited…