Center-of-Mass-based Robust Grasp Planning for Unknown Objects Using Tactile-Visual Sensors
arXiv:2006.00906
Abstract
An unstable grasp pose can lead to slip, thus an unstable grasp pose can be predicted by slip detection. A regrasp is required afterwards to correct the grasp pose in order to finish the task. In this work, we propose a novel regrasp planner with multi-sensor modules to plan grasp adjustments with the feedback from a slip detector. Then a regrasp planner is trained to estimate the location of center of mass, which helps robots find an optimal grasp pose. The dataset in this work consists of 1 025 slip experiments and 1 347 regrasps collected by one pair of tactile sensors, an RGB-D camera and one Franka Emika robot arm equipped with joint force/torque sensors. We show that our algorithm can successfully detect and classify the slip for 5 unknown test objects with an accuracy of 76.88% and a regrasp planner increases the grasp success rate by 31.0% compared to the state-of-the-art vision-based grasping algorithm.
arXiv comment6 pages + references
References in corpus (9)
- Scikit-learn: Machine Learning in Python
- More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
- Center-of-Mass-Based Grasp Pose Adaptation Using 3D Range and Force/Torque Sensing
- Classification based Grasp Detection using Spatial Transformer Network
- Fully Convolutional Grasp Detection Network with Oriented Anchor Box
- Slip Detection with Combined Tactile and Visual Information
- Slip Detection: Analysis and Calibration of Univariate Tactile Signals
- Real-world Multi-object, Multi-grasp Detection
- Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects