English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a robotic pick-and-place system that is capable ofgrasping and recognizing both known and novel objects in clutteredenvironments. The key new feature of the system is that it handles a wide rangeof object categories without needing any task-specific training data for novelobjects. To achieve this, it first uses a category-agnostic affordanceprediction algorithm to select and execute among four different graspingprimitive behaviors. It then recognizes picked objects with a cross-domainimage 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 withoutrequiring any additional training data. Exhaustive experimental resultsdemonstrate that our multi-affordance grasping achieves high success rates fora wide variety of objects in clutter, and our recognition algorithm achieveshigh accuracy for both known and novel grasped objects. The approach was partof the MIT-Princeton Team system that took 1st place in the stowing task at the2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models areavailable online at http://arc.cs.princeton.edu

Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching