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Deformability-Based Grasp Pose Detection From a Visible Image
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AI
DOI：10.1109/ACCESS.2024.3511546.png)
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Abstract
En 中文
In this study, we introduce a method for estimating deformability from the appearance of an object and a technique for detecting the grasp pose using a single-depth image. Deformability, defined as a score, provides an approximate measure of the ease with which an object can be deformed, based on the intuitive human assumption that appearance correlates with this property. Unlike previous approaches that synthesize the entire image in one step and struggle with accuracy in complex scenarios, such as cluttered environments with multiple objects, our method advances the field by employing an encoder-decoder model for simultaneous deformability estimation and instance segmentation from depth images, as well as by enhancing grasp pose detection for deformable objects under specific conditions using the outcomes of our deformability assessments. We demonstrated the efficacy of our integrated approach in diverse challenging settings, including the manipulation of deformable hollow objects in cluttered scenes, interaction with partially deformable objects, and displacement of nearby deformable objects in real-world environments.
Keywords:
Estimation
Deformable models
Shape
Robots
Grasping
Deep learning
Training
Visualization
Object recognition
Instance segmentation
deformable objects
deformability estimation