Proprioception-Based Grasping for Unknown Objects Using a Series-Elastic-Actuated Gripper
This addresses the challenge of robotic grasping for unknown objects, which is incremental as it builds on existing sensor-based and compliance methods.
The paper tackled the problem of grasping unknown objects by using proprioception (joint position and torque sensing) with a series-elastic-actuated gripper, achieving versatile performance including stable fingertip grasps, enveloping grasps, and transitions between them.
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.