Human-Planned Robotic Grasp Ranges: Capture and Validation
This addresses the challenge of teaching robots human-like grasping skills, though it is incremental as it partially improves data capture and generalization.
The paper tackles the problem of inefficient and limited human grasp data for robotic manipulation by introducing a protocol where participants specify valid grasp ranges, achieving 93.75% success in physical robot tests.
Leveraging human grasping skills to teach a robot to perform a manipulation task is appealing, but there are several limitations to this approach: time-inefficient data capture procedures, limited generalization of the data to other grasps and objects, and inability to use that data to learn more about how humans perform and evaluate grasps. This paper presents a data capture protocol that partially addresses these deficiencies by asking participants to specify ranges over which a grasp is valid. The protocol is verified both qualitatively through online survey questions (where 95.38% of within-range grasps are identified correctly with the nearest extreme grasp) and quantitatively by showing that there is small variation in grasps ranges from different participants as measured by joint angles, contact points, and position. We demonstrate that these grasp ranges are valid through testing on a physical robot (93.75% of grasps interpolated from grasp ranges are successful).