Real-World Multiobject, Multigrasp Detection

By Fu-Jen Chu, Ruinian Xu, Patricio A. Vela. 2018

377 citations recorded by the source corpus.

A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining the learning problem to be classified with null hypothesis competition instead of regression, the deep neural network with red, green, blue and depth (RGB-D) image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The method outperforms state-of-the-art approaches on the Cornell dataset with 96.0% and 96.1% accuracy on imagewise and objectwise splits, respectively. Evaluation on a multiobject dataset illustrates the generalization capability of the architecture. Grasping experiments achieve 96.0% grasp localization and 89.0% grasping success rates on a test set of household objects. The real-time process takes less than 0.25 s from image to plan.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering, Reinforcement Learning

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Citations in the corpus

Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Engineered success detectors can operate on ground-truth state in simulation, while learned success detectors can be trained on real examples of successes and failures in the real world [78, 79, 80, 81, 82].”
AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains (2022, background): “…for grasp pose detection evolved with the development of learning-based methods, including a point-based representation [15], [16], [17], a point pair representation [18], a rectangle representation [5], [19], [20], [21], [22], [23], [24], a grasp quality maps representation [25], etc.”
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network (2019, background): “[21] propose a novel architecture that can predict multiple grasps for multiple objects simultaneously rather than for a single object.”
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping (2020, methodology): “Previous datasets annotated by human [16, 50, 7] are usually small in scale and only provide sparse annotations.”
A Joint Network for Grasp Detection Conditioned on Natural Language Commands (2021, methodology): “2) Cascade (Ret-Gr): A cascading state-of-art natural language object retrieval model [5] and Multigrasp [22].”