A User Study on Personalized Stiffness Control and Task Specificity in Physical Human–Robot Interaction

Abstract

An ideal physical human-robot interaction (pHRI) should offer the users robotic systems that are easy to handle, intuitive to use, ergonomic and adaptive to human habits and preferences. But the variance in the user behavior is often high and rather unpredictable, which hinders the development of such systems. This article introduces a Personalized Adaptive Stiffness controller for pHRI that is calibrated for the user's force profile and validates its performance in an extensive user study with 49 participants on two different tasks. The user study compares the new scheme to conventional fixed stiffness or gravi tation compensation controllers on the 7DOF KUKA LWR IVb by employing two typical jointmanipulation tasks. The results clearly point out the importance of considering task specific parameters and human specific parameters while designing control modes for pHRI. The analysis shows that for simpler tasks a standard fixed controller may perform sufficiently well and that respective task dependency strongly prevails over individual differences. In the more complex task, quantitative and qualitative results reveal differ ences between the respective control modes, where the Personalized Adaptive Stiffness controller excels in terms of both performance gain and user preference. Further analysis shows that human and task parameters can be combined and quantified by considering the manipulability of a simplified human arm model. The analysis of user's interaction force profiles confirms this finding.

Trends in Haptic Communication of Human-Human Dyads: Toward Natural Human-Robot Co-manipulation

“…Ikeura et al ( 2002 ) implemented variable impedance control for lifting an object and proved that it was effective in completing the task. Several researchers have continued similar development using algorithms related to impedance control, such as Gopinathan et al ( 2017 ), who found that adaptive stiffness control increased performance over fixed stiffness and gravity compensated control. One of the problems with impedance control is that it usually requires off-line tuning.…”

Objective Assessment of Human Workload in Physical Human-robot Cooperation Using Brain Monitoring

“…In general, the continuous classification of operator workload can be employed in two different directions: either as an offline workload analysis tool or as a real-time adaptation feedback. Two potential areas that can benefit from an offline workload analysis are the assessment of different variable impedance/admittance control methods across different tasks, and the personalization of pHRI (e.g., subject-specific admittance control [Gopinathan et al 2017]).…”

A Custom EOG-Based HMI Using Neural Network Modeling to Real-Time for the Trajectory Tracking of a Manipulator Robot

“…People with severe disabilities cannot move their lower and upper extremities, so designing interfaces with custom features has become a technological challenge (Lum et al, 2012 ); for this reason, controllers have been implemented that can adapt to the needs of the user using haptic algorithms, multimodal human–machine interfaces (mHMI) and incorporation of artificial intelligence algorithms (Dipietro et al, 2005 ) among others. In Gopinathan et al ( 2017 ), a study is presented that describes the physical human–robot interaction (pHRI) using a custom rigidity control system of a 7-DOF KUKA industrial robot; the system is calibrated using a force profile obtained through each user and validates their performance by 49 participants using a heuristic control. A similar control system is applied in Buchli et al ( 2011 ), where the level of force of each user is adapted to the control of a 3-DOF robot by haptics and is adjusted to the biomechanics of the user, in order to work on cooperative environments with humans (Gopinathan et al, 2017 ).…”

“…In Gopinathan et al ( 2017 ), a study is presented that describes the physical human–robot interaction (pHRI) using a custom rigidity control system of a 7-DOF KUKA industrial robot; the system is calibrated using a force profile obtained through each user and validates their performance by 49 participants using a heuristic control. A similar control system is applied in Buchli et al ( 2011 ), where the level of force of each user is adapted to the control of a 3-DOF robot by haptics and is adjusted to the biomechanics of the user, in order to work on cooperative environments with humans (Gopinathan et al, 2017 ).…”