Human grasp position estimation for human–robot cooperative object manipulation

Artikel i vetenskaplig tidskrift, 2020

This paper addresses the problem of human grasp position estimation in a physical human–robot object handling scenario. The problem is formulated as a linear regression by considering the human grasp position and their exerted torque as unknown parameters. We propose a modified least-squares algorithm to estimate the parameters by evaluating the quality of the estimates based on the assumption that the parameters should remain constant for a period of time. The solution is model-agnostic in terms of the human force/torque model – requiring only force/torque measurements on the robot side and proprioception – and is model-based in terms of the object model. The proposed grasp position estimation method is compared statistically with a conventional contact point estimation method using the collected experimental data. Moreover, the performance of the developed method is evaluated through various scenarios of physical human–robot interaction.