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Grasp State Assessment of Deformable Objects Using Visual-Tactile Fusion Perception
Humans can quickly determine the force required to grasp a deformable ob...
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6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
Generating grasp poses is a crucial component for any robot object manip...
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Towards Affordance Prediction with Vision via Task Oriented Grasp Quality Metrics
While many quality metrics exist to evaluate the quality of a grasp by i...
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Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
A new generation of automated bin picking systems using deep learning is...
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Generating Optimal Grasps Under A Stress-Minimizing Metric
We present stress-minimizing (SM) metric, a new metric of grasp qualitie...
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Deep Differentiable Grasp Planner for High-DOF Grippers
We present an end-to-end algorithm for training deep neural networks to ...
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Center-of-Mass-Based Grasp Pose Adaptation Using 3D Range and Force/Torque Sensing
Lifting objects, whose mass may produce high wrist torques that exceed t...
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Minimal Work: A Grasp Quality Metric for Deformable Hollow Objects
Robot grasping of deformable hollow objects such as plastic bottles and cups is challenging as the grasp should resist disturbances while minimally deforming the object so as not to damage it or dislodge liquids. We propose minimal work as a novel grasp quality metric that combines wrench resistance and the object deformation. We introduce an efficient algorithm to compute required work to resist an external wrench for a manipulation task by solving a linear program. The algorithm first computes the minimum required grasp force and an estimation of the gripper jaw displacements based on the object deformability at different locations measured with physical experiments. The work done by the jaws is the product of the grasp force and the displacements. The grasp quality metric is computed based on the required work under perturbations of grasp poses to address uncertainties in actuation. We collect 460 physical grasps with a UR5 robot and a Robotiq gripper. Physical experiments suggest the minimal work quality metric reaches 74.2 accuracy and is up to 24.2 where the balanced accuracy is the raw accuracy normalized by the number of successful and failed real-world grasps.
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