Robotic Grasping
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Benchmarks
Most implemented
Rethinking the Inception Architecture for Computer Vision
Grasping Field: Learning Implicit Representations for Human Grasps
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
Papers
Beyond the Current Scene: Event-Referential Grasping with Active View Selection
A robot that observes people interacting with objects should be able to carry out later requests that refer back to those interactions. Such requests may specify a grasp target by the role it played in a past event rathe…
Robotic GraspingARTiS: An Adaptive Robotic Gripper for Enhanced Tool Manipulation in Disassembly Applications
Grasping and holding tools while using them presents a considerable challenge not only for robots but also for humans. Such a challenge is particularly noticeable in processes involving assembly and disassembly, where ef…
Robotic GraspingFast Generative Grasping via Lie Group-Constrained MeanFlow
Grasp synthesis is a core task in robotic manipulation, for which the solution typically forms a multimodal distribution rather than a point estimate. Generative robotic grasping aims to learn this distribution with deep…
Domain AdaptationRobotic GraspingPartialBiGrasp: Inferring Hidden Local Geometry for Bimanual Grasping from Partial Views
Dual-arm robotic grasping is essential for manipulating large, heavy, and geometrically complex objects that cannot be reliably handled using a single manipulator. These large objects often contain only sparse graspable …
Robotic GraspingPoint CloudsIterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision
Developing robots capable of understanding and manipulating objects requires compact, interpretable, and generalizable representations. This work proposes a reinforcement learning-based framework for robotic grasp refine…
Reinforcement LearningRobotic GraspingSeededGrasp: Language-Guided Grasping in Complex Scenes with Multiple Embodiments
Practical robotic grasping in complex scenes requires both 3D spatial reasoning and alignment with task-specific requirements. Vision-language models (VLMs) offer a natural way to specify these requirements using languag…
Spatial ReasoningRobotic Grasping