Abi-Farraj, Firas

Pacchierotti, Claudio

Robuffo Giordano, Paolo

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Human-subject Evaluation of Shared-Control Approaches for Robotic Telemanipulation

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HAL Id: hal-01691114https://hal.inria.fr/hal-01691114Submitted on 23 Jan 2018HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Human-subject Evaluation of Shared-ControlApproaches for Robotic TelemanipulationFiras Abi-Farraj, Claudio Pacchierotti, Paolo Robuffo GiordanoTo cite this version:Firas Abi-Farraj, Claudio Pacchierotti, Paolo Robuffo Giordano. Human-subject Evaluation of Shared-Control Approaches for Robotic Telemanipulation. 2017 IEEE/RSJ IROS Workshop Human in-the-loop robotic manipulation: on the influence of the human role, Sep 2017, Vancouver, Canada. ￿hal-01691114￿Human-subject Evaluation of Shared-Control Approachesfor Robotic TelemanipulationFiras Abi-Farraj, Claudio Pacchierotti and Paolo Robuffo GiordanoI. INTRODUCTIONRemote telemanipulation has shown significant advance-ments over the last years and promising results have beenachieved in several robotic tasks [1], [2]. Handling radioactivewaste in nuclear decommissioning sites is a an importantexample of such a task where telemanipulation systems arealready heavily used. However, currently-employed systemsare still extremely primitive, making the handling of thesematerials prohibitively slow and ineffective. As the estimatedcost for the decommissioning and clean-up of nuclear siteskeeps rising, it is clear that one would need faster and moreeffective approaches.Towards this goal, several recent works have been presentedin the context of the European H2020 Robotic Manipulationfor Nuclear Sort and Segregation (RoMaNS) project [3], [4].We present, in this context, the evaluation of a haptic-enabledshared-control architecture for telemanipulation. It was in-spired by the work of Abi-Farraj et al. [5], who presentedthe algorithmic design of the shared-control architecture, butwithout providing any user evaluation. The experimental setupis shown in Fig. 1. The master system is composed of theHaption Virtuose 6D haptic device, a high performance forcefeedback device with three active translational DoF and threeactive rotational DoF. The slave system is composed of a6-DoF Viper S850 robotic arm carrying a pneumatic parallelgripper. A wooden object is placed on a table in front of therobotic manipulator. An autonomous algorithm is in chargeof regulating a subset of the manipulator DoF to help thehuman operator in grasping the object. At the same time,the human operator steers the robotic end-effector along theremaining null-space directions with respect to the main taskusing the Virtuose haptic interface.II. SHARED CONTROL ARCHITECTUREIn our implementation, the autonomous algorithm controls2 DoF of the robotic manipulator, keeping the gripper alwaysoriented toward the object to grasp. The gripper is thereforeconstrained to move on the surface of a virtual sphere centeredon the object. The human operator is then able to move thegripper across the surface of the sphere and toward/away fromthe object (i.e., changing the radius of the sphere), controllingthe remaining 4 DoF of the robot.To this end, let s be the variable designating the directionfrom the gripper to the object s = GPO/‖GPO‖ ∈ S2. Theautonomous algorithm drives s to be aligned with the z-axisof the gripper in order to keep the gripper oriented towardsthe object as described before. The control law governing theF. Abi Farraj, C. Pacchierotti, and P. Robuffo Giordano are with the CNRSat Irisa and Inria Rennes Bretagne Atlantique, Rennes, France.(a) Master side. (b) Slave side.Fig. 1. Experimental setup.motion of the gripper will then beV G =[vGωG]= kGL†s(ZG − s) +n∑i=1λini, kG > 0,(1)where kG is a control gain, L†s represents the Moore-Penrosepseudo-inverse of the interaction matrix Ls associated withs (ṡ = LsV G), ZG is the axis associated with the gripper(fig. 1), λ = [. . . λi . . .] ∈ R4 indicates the pseudo-velocitycommands of the human operator and NB = [. . .ni . . .] ∈R6×4 is a basis spanning the null space of Ls.On the master side, the force feedback received by theoperator mainly aims at keeping the user away from pre-defined constraints (singularities and joint limits in ourscenario), which are described by a cost function H . Thecorresponding force cues are then defined byτ = −BM ẋM −(∂H(q)∂q)TJ−1G(q)ni, (2)i.e., by projecting the joint motion caused by the i-th null-space direction ni onto the negative gradient of H(q). MatrixJG(q) ∈ R6×6 is the geometric Jacobian of the manipulator.BM ∈ R4×4 is a positive definite matrix indicating thedamping factor and xM represents the master device’sconfiguration vector.III. EXPERIMENTAL EVALUATIONA. Experimental conditions, task, and participantsParticipants were required to control the motion of therobotic manipulator and gripper to grasp the wooden pieceand lift it from the ground.We considered two different ways of commanding themotion of the robot through the haptic interface1) Position-Velocity (PV): positions of the haptic deviceare mapped into velocities of the manipulator2) Velocity-Velocity (VV): velocities of the haptic deviceare mapped into velocities of the manipulator.020406080(a) Completion time (s)00.511.522.5(b) Trajectory length (m)0100200300400(c) Angular motion (deg)0246810(d) Perceived effectivessFig. 2. Experimental evaluation. Mean and 95% confidence interval of (a)completion time, (b) trajectory length, (c) angular motion, and (d) perceivedeffectiveness of the four feedback conditions are plotted.Moreover, two different levels of human involvement inthe control are considered (shared control vs. teleoperation)1) Shared Control (S): the subject controls only 4 DoF ofthe manipulator2) Teleoperation (T): the subject controls all 6 DoF of themanipulator.We end up with four different experimental conditionsrepresenting the four possible combinations of the describedcontrol and commanding modes: S+PV, S+VV, T+PV andT+VV. The shared-control architectures, employed in condi-tions S+PV and S+VV, are summarized in Sec. I and describedin details in [5]. In T+PV and T+VV conditions, the subjectis in full control of the manipulator’s 6 DoF.Ten subjects participated in the study, carrying out 2repetitions of the grasping task per experimental condition.B. ResultsWe recorded (i) the completion time, (ii) the lineartrajectory followed by the robotic end-effector, (iii) theangular motion of the robotic end-effector, and (iv) theperceived effectiveness of the different conditions. To comparethe different metrics, we ran both two-way and one-wayrepeated-measures ANOVA tests on the data. All data passedthe Shapiro-Wilk normality test.Figure 2a shows the average task completion time. The two-way ANOVA test revealed a statistically significant changein the task completion time for the human involvement inthe control variable (shared control vs. teleoperation, F(1, 9)= 25.852, p = 0.001). The one-way ANOVA test revealed astatistically significant change in the task completion timeacross the conditions (F(3, 27) = 9.312, p < 0.001). Post hocanalysis with Bonferroni adjustments revealed a statisticallysignificant difference between S+V vs. T+V (p = 0.030),S+V vs. T+P (p = 0.035), S+P vs. T+V (p = 0.031), andS+P vs. T+P (p = 0.025).Figure 2b shows the average linear motion covered by therobotic gripper during the task. The two-way ANOVA testrevealed a statistically significant change in the trajectorylength for both the human involvement in the control (F(1, 9)= 30.968, p < 0.001) and the motion control type (velocityvs. position, F(1, 9) = 9.035, p = 0.015) variables. The one-way ANOVA test revealed a statistically significant change inthe trajectory length across the conditions (F(1.929, 17.360)= 14.072, p < 0.001). Post hoc analysis with Bonferroniadjustments revealed a statistically significant differencebetween S+V vs. S+P (p = 0.049), S+V vs. T+V (p = 0.043),S+V vs. T+P (p = 0.002), and S+P vs. T+P (p = 0.012).Figure 2c shows the average angular motion covered bythe robotic gripper during the task. The two-way ANOVAtest revealed a statistically significant change in the angularmotion for both the human involvement in the control(F(1, 9) = 39.350, p < 0.001) and the motion controltype (position-velocity vs. velocity-velocity, F(1, 9) = 8.202,p = 0.015) variables. The one-way ANOVA test revealed astatistically significant change in the trajectory length acrossthe conditions (F(3, 27) = 12.994, p < 0.001). Post hocanalysis with Bonferroni adjustments revealed a statisticallysignificant difference between S+V vs. S+P (p = 0.025), S+Vvs. T+V (p = 0.007), S+V vs. T+P (p = 0.001), S+P vs.T+V (p = 0.039), and S+P vs. T+P (p = 0.005).Fig. 2d shows the perceived effectiveness for the fourexperimental conditions, asked using bipolar Likert-type nine-point scales. A Friedman test showed a statistically significantdifference between the means of the four feedback conditions(χ2(3) = 26.753, p < 0.001, a = 0.05). The Friedman test isthe non-parametric equivalent of the more popular repeated-measures ANOVA. The latter is not appropriate here since thedependent variable was measured at the ordinal level. Post hocanalysis with Bonferroni adjustments revealed a statisticallysignificant difference between S+V vs. T+V (p < 0.001),S+V vs. T+P (p = 0.026), S+P vs. T+V (p < 0.001), andS+P vs. T+P (p = 0.044).IV. CONCLUSIONSResults showed that, in all the considered metrics, theshared-control approach significantly outperformed the moreclassic teleoperation architecture. Moreover, all the subjectspreferred the shared-control architecture with respect to tele-operation. This proves our hypothesis that shared-control canbe a viable and very effective approach to improve currently-available teleoperation systems in remote manipulation tasks.REFERENCES[1] E. Nuño, L. Basañez, and R. Ortega, “Passivity-based control for bilateralteleoperation: A tutorial,” vol. 47, no. 3, pp. 485–495, 2011.[2] J. Abbott, P. Marayong, and A. Okamura, “Haptic virtual fixtures forrobot-assisted manipulation,” Robotics Research, pp. 49–64, 2007.[3] F. Abi-Farraj, T. Osa, N. Pedemonte, J. Peters, G. Neumann, and P. G.Robuffo, “A learning-based shared control architecture for interactivetask execution,” in IEEE Int. Conf. on Robotics and Automation, 2017.[4] A. M. G. Esfahani, F. Abi-Farraj, P. R. Giordano, and R. Stolkin,“Human-in-the-loop optimisation: mixed initiative grasping for optimallyfacilitating post-grasp manipulative actions,” arXiv:1707.08147, 2017.[5] F. Farraj, N. Pedemonte, and P. Robuffo Giordano, “A visual-basedshared control architecture for remote telemanipulation,” in IEEE/RSJInt. Conf. on Intelligent Robots and Systems, 2016.

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Human-subject Evaluation of Shared-Control Approaches for Robotic Telemanipulation

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