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Found 4 papers, 4 papers with code
LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments
Instructing a robot to complete an everyday task within our homes has been a long-standing challenge for robotics.
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
In this work, we focus on the instance segmentation task and provide a comprehensive study of different techniques that allow adapting an object segmentation model in presence of novel objects or different domains.
Fast Object Segmentation Learning with Kernel-based Methods for Robotics
Our approach is validated on the YCB-Video dataset which is widely adopted in the computer vision and robotics community, demonstrating that we can achieve and even surpass performance of the state-of-the-art, with a significant reduction (${\sim}6\times$) of the training time.
Fast Region Proposal Learning for Object Detection for Robotics
This shortens training time while maintaining state-of-the-art performance.
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