1,133 research outputs found
Probabilistic multi-class segmentation for the Amazon picking challenge
We present a method for multi-class segmentation from RGB-D data in a realistic warehouse picking setting. The method computes pixel-wise probabilities and combines them to find a coherent object segmentation. It reliably segments objects in cluttered scenarios, even when objects are translucent, reflective, highly deformable, have fuzzy surfaces, or consist of loosely coupled components. The robust performance results from the exploitation of problem structure inherent to the warehouse setting. The proposed method proved its capabilities as part of our winning entry to the 2015 Amazon Picking Challenge. We present a detailed experimental analysis of the contribution of different information sources, compare our method to standard segmentation techniques, and assess possible extensions that further enhance the algorithm’s capabilities. We release our software and data sets as open source
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
This paper presents a robotic pick-and-place system that is capable of
grasping and recognizing both known and novel objects in cluttered
environments. The key new feature of the system is that it handles a wide range
of object categories without needing any task-specific training data for novel
objects. To achieve this, it first uses a category-agnostic affordance
prediction algorithm to select and execute among four different grasping
primitive behaviors. It then recognizes picked objects with a cross-domain
image classification framework that matches observed images to product images.
Since product images are readily available for a wide range of objects (e.g.,
from the web), the system works out-of-the-box for novel objects without
requiring any additional training data. Exhaustive experimental results
demonstrate that our multi-affordance grasping achieves high success rates for
a wide variety of objects in clutter, and our recognition algorithm achieves
high accuracy for both known and novel grasped objects. The approach was part
of the MIT-Princeton Team system that took 1st place in the stowing task at the
2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are
available online at http://arc.cs.princeton.eduComment: Project webpage: http://arc.cs.princeton.edu Summary video:
https://youtu.be/6fG7zwGfIk
Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge
Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu
Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge
Robot warehouse automation has attracted significant interest in recent years, perhaps most visibly in the Amazon Picking Challenge (APC) [1]. A fully autonomous warehouse pick-and-place system requires robust vision that reliably recognizes and locates objects amid cluttered environments, self-occlusions, sensor noise, and a large variety of objects. In this paper we present an approach that leverages multiview RGB-D data and self-supervised, data-driven learning to overcome those difficulties. The approach was part of the MIT-Princeton Team system that took 3rd- and 4th-place in the stowing and picking tasks, respectively at APC 2016. In the proposed approach, we segment and label multiple views of a scene with a fully convolutional neural network, and then fit pre-scanned 3D object models to the resulting segmentation to get the 6D object pose. Training a deep neural network for segmentation typically requires a large amount of training data. We propose a self-supervised method to generate a large labeled dataset without tedious manual segmentation. We demonstrate that our system can reliably estimate the 6D pose of objects under a variety of scenarios. All code, data, and benchmarks are available at http://apc.cs.princeton.edu
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