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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We achieved contact-rich flexible object manipulation, which was difficult tocontrol with vision alone. In the unzipping task we chose as a validation task,the gripper grasps the puller, which hides the bag state such as the directionand amount of deformation behind it, making it difficult to obtain informationto perform the task by vision alone. Additionally, the flexible fabric bagstate constantly changes during operation, so the robot needs to dynamicallyrespond to the change. However, the appropriate robot behavior for all bagstates is difficult to prepare in advance. To solve this problem, we developeda model that can perform contact-rich flexible object manipulation by real-timeprediction of vision with tactility. We introduced a point-based attentionmechanism for extracting image features, softmax transformation for predictingmotions, and convolutional neural network for extracting tactile features. Theresults of experiments using a real robot arm revealed that our method canrealize motions responding to the deformation of the bag while reducing theload on the zipper. Furthermore, using tactility improved the success rate from56.7% to 93.3% compared with vision alone, demonstrating the effectiveness andhigh performance of our method.

Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility