Proprioception-Based Grasping for Unknown Objects Using a Series-Elastic-Actuated Gripper

Abstract

Grasping unknown objects has been an active research topic for decades. Approaches range from using various sensors (e.g. vision, tactile) to gain information about the object, to building passively compliant hands that react appropriately to contacts. In this paper, we focus on grasping unknown objects using proprioception (the combination of joint position and torque sensing). Our hypothesis is that proprioception alone can be the basis for versatile performance, including multiple types of grasps for objects with multiple shapes and sizes, and transitions between grasps. Using a series-elastic-actuated gripper, we propose a method for performing stable fingertip grasps for unknown objects with unknown contacts, formulated as multi-input-multi-output (MIMO) control. We also show that the proprioceptive gripper can perform enveloping grasps, as well as the transition from fingertip grasps to enveloping grasps.

Vision for Robust Robot Manipulation

“…Alternatively, information about joint position, joint velocity or joint torque ( proprioception ), has been often used for robot grasping [15,16]. Nevertheless, the grasp stability may be affected by several parameters such as the configuration of the robotic gripper, the (mis)alignment of the joint axes, or inaccurate data (e.g., open/close instead of the exact grip aperture).…”