VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation

Citations Over TimeTop 10% of 2020 papers

Abstract

Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks. We extend the Visual Foresight framework to learn fabric dynamics that can be efficiently reused to accomplish different fabric manipulation tasks with a single goal-conditioned policy. We introduce VisuoSpatial Foresight (VSF), which builds on prior work by learning visual dynamics on domain randomized RGB images and depth maps simultaneously and completely in simulation. We experimentally evaluate VSF on multi-step fabric smoothing and folding tasks against 5 baseline methods in simulation and on the da Vinci Research Kit (dVRK) surgical robot without any demonstrations at train or test time. Furthermore, we find that leveraging depth significantly improves performance. RGBD data yields an 80% improvement in fabric folding success rate over pure RGB data. Code, data, videos, and supplementary material are available at https://sites.google.com/view/fabric-vsf/.

Related Papers

• → An Object Detection and Pose Estimation Approach for Position Based Visual Servoing(2017)5 cited
• → Self-monitoring to improve robustness of 3D object tracking for robotics(2011)4 cited
• → Object-oriented stripe structured-light vision-guided robot(2017)2 cited
• → 3-D tracking of a moving object by an active stereo vision system(2002)9 cited
• → Robust object tracking based on RGB-D camera(2014)