Haptic Negotiation and Role Exchange for Collaboration in Virtual Environments

1. Haptic Negotiation and Role Exchange for Collaboration in VirtualEnvironments S. O. Oguz, A. Kucukyilmaz, T. M. Sezgin, and C. Basdogan Presenter : SunghoonYim, HVR

2. Contents • Motor learning • Haptic negotiation and role exchange • Experiment • Discussion and conclusion

3. Motor Learning • Motor learning is a set of processes associated with practice or experience leading to relatively permanent changes in the capability for movement. • Motor learning is not directly observable. Performance Curve

4. Designing Experiments on Motor Learning • Goal of experiment • Which independent variables are involved in maximizing learning • Training methods are independent variables of motor learning experiment • Transfer or retention designs • Participants of a motor learning experiment are given a retention interval of sufficient duration. • The zunder a common level of the independent variable – under identical conditions. • Retention test : the same task as practiced • Transfer test : new variation of tasks practiced

5. Augmented Feedback • Inherent feedback • Information about people’s own movement for task through various sensory channels. • Augmented feedback • Information provided about task that is supplemental to, or that augments, the inherent feedback. • Haptic guidance can be a kind of augmented feedback

6. Effects of Augmented Feedback • Augmented feedback can enhance learning • Provides information • Enhances to form association between movement parameters and resulting actions • Plays a motivational role • Augmented feedback can degrade learning • Blocks the processing of inherent sources of feedback • Induces maladaptive corrections

7. Issues of Augmented Feedback • Types of information • KP (knowledge of performance) : information about the form of the movement • KR (knowledge of results) : post-movement information about performance outcome • Amounts of information • Providing too much information is ineffective • Frequency of augmented feedback • Too frequent feedback may cause the learner to become dependent on externally presented information • Bandwidth KR, faded KR, and summary KR

8. Haptic Feedback for Motor Learning • Facilitating the learning of complex motor skills by providing haptic feedback • Haptic feedback has been expected as more effective modality for motor learning Haptic feedback Auditory feedback “lift your brush more” This message must be translated from the auditory system to the proprioceptive system Haptic feedback directly induces the desired movements of a learner

9. Contents • Motor learning • Haptic negotiation and role exchange • Experiment • Discussion and conclusion

10. Collaboration of Human-Robot • Collaboration • Working jointly with others or together especially in an intellectual endeavor • Defining roles in collaboration • In human-human collaboration, one plays a dominant role (conductor) and the other one plays none dominant role (executor) • In human-robot collaboration, human plays a dominant role, and this will not change all along the task execution.

11. Role Exchanges and Negotiation • Role exchanges • The contribution to the motion of the manipulated object can be distributed between partners. • Each partner can take on one, both or none of the conductor or executor roles • Negotiation • In order to archive shared goal, two partners should create an agreement upon their courses of actions. • Negotiation is required to make this agreement.

12. Target Task : Haptic Board Game • The Haptic Board Game involves controlling the position of a ball on a flat board to reach arbitrarily positioned targets with the help of a haptic device

13. Physical Model of Shared Control • The physical model is composed of simple mass-spring-damper system. • Each point is regarded as mass-less particle

14. System Model Conditions • Both axes guidance (BG) • Both user and the computer have control on both axes, and each affects the system equally • The user feels the forces applied to the ball by the controller. • Role exchange (RE) • The computer negotiates with the user, based on the user’s force profile, to decide on how they should share control. • No guidance (NG) • No haptic guidance is given to control the ball position on the board.

15. System Model Conditions (cont’d)

16. Flow of Interaction within the Physical Model

17. Model of Role Exchange • The system is designed to allow haptic negotiation between partners by sensing the user’s intentions. • Role exchange occurs whenever the magnitude of the user applied force exceeds the threshold values for over a predetermined amount of time

18. Model of Role Exchange (cont’d) • Role exchange applied each axis independently.

19. Contents • Motor learning • Haptic negotiation and role exchange • Experiment • Discussion and conclusion

20. Experiment Design and Goals • Main hypotheses • Role exchange has measurable benefits over other conditions. • Users will subjectively prefer role exchange over other conditions. • Design • 10 participants. • Within-subject experiment (!). • At least three days between two successive experiments • Unbalanced trials (!!). • In BG and NG, 15 times for a experiment • In RE, 3+15 times for a experiment!

21. Metrics • Subjective metrics • Performance, human-likeness, collaboration, degree of user control, degree of computer control. • All metrics except performance (a 5-point Likert scale) were measured by a 7-point Likert scale. • Ideal path and integral of time and absolute magnitude of error (ITAE) • Work done by the user due to the spring located between NIP and HIP

22. Results • Subjective results • ITAE and Work

23. Results (cont’d) • Average number of role exchanges of each user over and average time spent by users in each controller state S1 : user dominant , S2 : role blending, S3 : equal control

24. Contents • Motor learning • Haptic negotiation and role Exchange • Experiment • Discussion and conclusion

25. Discussion • From the results • I can’t believe any results from this experiment. • Bad experiment design and statistical analysis • Omni would be too low-end performance for this task. • The Haptic Board Game task would be great application to apply haptic guidance or disturbance.

26. Discussion (cont’d) • Progressive haptic guidance scheme • The dependency of participants on the guidance by adjusting the control gains based on individual participant performance • Role exchange • Employs real time adjusting of the control gains based on the participant’s intention • Role exchange can be used for progressive haptic guidance. • NG <-> BG based on the participant’s performance

27. Conclusion • Great idea! But poor evaluation. • We can use the role exchange methodology and the haptic board game.