相关论文: Task-Oriented Dexterous Hand Pose Synthesis Using …

This paper describes a method for generating robot grasps by jointly considering stability and other task and object-specific constraints. We introduce a three-level representation that is acquired for each object class from a small number…

Robust and human-like dexterous grasping of general objects is a critical capability for advancing intelligent robotic manipulation in real-world scenarios. However, existing reinforcement learning methods guided by grasp priors often…

Inherent morphological characteristics in objects may offer a wide range of plausible grasping orientations that obfuscates the visual learning of robotic grasping. Existing grasp generation approaches are cursed to construct discontinuous…

Task-oriented grasping, which involves grasping specific parts of objects based on their functions, is crucial for developing advanced robotic systems capable of performing complex tasks in dynamic environments. In this paper, we propose a…

Grasping user-specified objects is crucial for robotic assistants; however, most current 6-DoF grasp detection methods are object-agnostic, making it challenging to grasp specific targets from a scene. To achieve that, we present GoalGrasp,…

Accurately estimating 3D hand pose is crucial for understanding how humans interact with the world. Despite remarkable progress, existing methods often struggle to generate plausible hand poses when the hand is heavily occluded or blurred.…

Measuring grasp stability is an important skill for dexterous robot manipulation tasks, which can be inferred from haptic information with a tactile sensor. Control policies have to detect rotational displacement and slippage from tactile…

Generating large-scale demonstrations for dexterous hand manipulation remains challenging, and several approaches have been proposed in recent years to address this. Among them, generative models have emerged as a promising paradigm,…

Despite advances in hand-object interaction modeling, generating realistic dexterous manipulation data for robotic hands remains a challenge. Retargeting methods often suffer from low accuracy and fail to account for hand-object…

Scaling dexterous robot learning is constrained by the difficulty of collecting high-quality demonstrations across diverse operators. Existing wearable interfaces often trade comfort and cross-user adaptability for kinematic fidelity, while…

Grasp detection methods typically target the detection of a set of free-floating hand poses that can grasp the object. However, not all of the detected grasp poses are executable due to physical constraints. Even though it is…

As robotics progresses toward general manipulation, dexterous hands are becoming increasingly critical. However, proprioception in dexterous hands remains a bottleneck due to limitations in volume and generality. In this work, we present…

Dexterous grasping remains a central challenge in robotics due to the complexity of its high-dimensional state and action space. We introduce T(R,O) Grasp, a diffusion-based framework that efficiently generates accurate and diverse grasps…

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…

Grasping and manipulating objects is an important human skill. Since most objects are designed to be manipulated by human hands, anthropomorphic hands can enable richer human-robot interaction. Desirable grasps are not only stable, but also…

Human hands possess the dexterity to interact with diverse objects such as grasping specific parts of the objects and/or approaching them from desired directions. More importantly, humans can grasp objects of any shape without…

One of the most important research challenges in upper-limb prosthetics is enhancing the user-prosthesis communication to closely resemble the experience of a natural limb. As prosthetic devices become more complex, users often struggle to…

We introduce UniGraspTransformer, a universal Transformer-based network for dexterous robotic grasping that simplifies training while enhancing scalability and performance. Unlike prior methods such as UniDexGrasp++, which require complex,…

Generating grasps for a dexterous hand often requires numerous grasping annotations. However, annotating high DoF dexterous hand poses is quite challenging. Especially for functional grasps, requiring the hand to grasp the object in a…

6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where objects may be textureless and in difficult poses, and…