Won Kyung Do, Bianca Aumann, Camille Chungyoun, and Monroe Kennedy III
The ability to grasp and manipulate small objects in cluttered environments remains a significant challenge. This paper introduces a novel approach that utilizes a tactile sensor-equipped gripper with eight degrees of freedom to overcome these limitations. We employ DenseTact 2.0 for the gripper, enabling precise control and improved grasp success rates, particularly for small objects ranging from 5mm to 25mm. Our integrated strategy incorporates the robot arm, gripper, and sensor to manipulate and orient small objects for subsequent classification effectively. We contribute a specialized dataset designed for classifying these objects based on tactile sensor output and a new control algorithm for in-hand orientation tasks. Our system demonstrates 88% of successful grasp and successfully classified small objects in cluttered scenarios.
Pipeline of the small-object grasping, pinching, classifying, and sorting process.
8-DOF gripper realistic model. The gripper can be attached directly to the Franka robot arm.
Joint limit of the gripper for each joint. Gripper maximizes the rolling manipulation on hemispherical end effector.
Classification result of screw objects and classified labels for 466 touches.
Classification result of small random objects and classified labels for 93 test touches.
We demonstrated our process with Franka arm. The right top image shows the depth estimation of the DenseTact tactile sensor, and the right bottom image shows the rgb image from the tactile sensor.
Experimental setup and demonstrated result of the whole process for 198 grasps.
Authors are members of the ARMLab. This work is supported by the National Science Foundation under Grants 2142773 and 2220867.