The 18th IEEE International Symposium on Robot and Human Interactive Communication Toyama, Japan, Sept. 27-Oct. 2, 2009
ThA4.4
Active and Efficient Motor Skill Learning Method Used in a Haptic Teleoperated System Nadia Garcia-Hernandez and Vicente Parra-Vega Abstract— This paper presents a new haptic training method for motor skill acquisition working in a teleoperated system. The method promotes a visuo-motor learning process base on the active attention and participation of the apprentice during training. Using a robot end-effector, the master describes a desired path which is followed by the apprentice using a haptic device. The master aids the apprentice by restricting his motion within upper and lower boundaries parallel to the path being created in real time. The boundaries are located at a given distance established by the master base on the apprentice learning improvement. Using this method, the master does not disturb or control the position and velocity of the apprentice directly which avoids apprentice dependence on the master. A training handdrawing scheme was designed and implemented to evaluate the visuo-haptic method; experimental results show faster and better apprentice’s motor performance than using only visual information. These results suggest a possible implementation of the method in complex motors tasks such as calligraphy or surgery, to name a few.
I. INTRODUCTION Motor skill can be defined as a human activity involving one or more movements performed with a high degree of precision and accuracy [1] and it can range from the complex (surgery) to more simple ones (cutting). A good and fast training of motor skills is a challenge task that has been treated from different perspectives and faced from several areas, for instance robotics, neuroscience, biomechanics, ergonomics, etc. Transferring motor skills is quite complex due to the involvement of the essentially nonlinear and unstable plant, the human. Nowadays, this paradigm possesses insurmountable scientific and technological problems because we know very little about the deterministic response of humans. Although, in learning new motor skills, visual feedback of the teacher’s motion provides to the apprentice a good understanding of the task, it has been prove that after short periods benefits of visual training disappear. Moreover, some studies suggest that a multimodal interaction scheme with visual and haptic information accelerates the learning process and improves apprentice performance [2][3]. Other study [4] have revealed that visual information helps to more precisely understand the shape and position of the tasks but timing accuracy is better achieve with haptic cues. In the last years, motor skills training systems have made use of force feedback devices to provide the haptic cues Manuscript received March 15, 2009. This work was not supported by any organization. N. Garcia is with Italian Institute of Technology, Advance Robotics Laboratory, Via Morego, 30, 16163 Genova, Italy
[email protected] V. Parra is with Center for Research and Advanced Studies, Robotics and Advanced Manufacture Division, Carretera Saltillo-Monterrey Km 13.5, Ramos Arizpe, Coahuila, 25900 Mexico
[email protected]
978-1-4244-5081-7/09/$26.00 ©2009 IEEE
required during visuo-motor learning tasks. They have also implemented different training control strategies in order to enhance a better transfer of skills. These strategies in general involve the complete or partial guidance of the apprentice towards the desired trajectory. In complete guidance, apprentice movements are totally controlled and in consequence the training occurs passively and this increases apprentice dependency on the teacher for performing the task. However, to achieve a more effective learning in tasks such as handwriting, it is required the active participation of the apprentice in choosing their trajectory and velocity profile in order to follow the desired path. Transfer of motor skills by a human instructor is often affective to transmit motor skills; however it faces different problems such as the limited time available for the apprentice to complete the training and the necessary physically presence of an instructor. The use of new technologies such as haptic devices tries to get these problems solved allowing different mediums for training: virtual or remote. They make possible the substitution of a human instructor with the one called ”virtual teacher” (virtual training) and also offer the possibility of a remote training with the presence of an instructor in a remote location (remote teleoperation training). The ”virtual teacher” strategy allows implementing a ”record and play” modality which registers an expert’s skill motion in a computer data base to be later replicated for the apprentice in several occasions. Although, an apprentice with a virtual teacher can have training sessions without limit of time and with a good level of precision, we believe that the presence of a teacher encourages more the apprentice attention and promotes better the visuo-motor learning. In this paper, an active and efficient motor skill training method is presented. The method uses a multimodal interaction (visual-haptic) framework to teach complex movements performed by a teacher (physically present at a local station) in a teleoperated scheme. The apprentice located at the remote station is actively involved in the learning process, he choose its position and velocity profile in order to follow the desired path. During training, the apprentice try to follow the desired path being performed by the master, while the master restricts his motion within upper and lower boundaries created parallel to the desired path and located at a given distance from the path. The teacher decides in real time which training strategy used base on the initial apprentice skills. Experimental results shows that the visual-haptic method proposed, using a real remote teacher, leads to a better and faster learning of 2D geometric patterns than when only visual modality was used. Accordingly, this method may be a fundamental tool in
915
complex motors tasks such as calligraphy, rehabilitation of motor disabilities or surgery, to name a few. This paper is organized as follows; section II discusses some related works, section III describes the system setup, section IV describes the training method proposed in this work, section V discusses the control design of the system, section VI explains the experiments. Results of the experiment and discussion of the results are depicted in section VII and finally section VIII addresses the conclusions and reports future activities.
Fig. 1. Experimental setup: Teleoperated training system which use an active and efficient visuo-haptic method to motor skill learning. The apprentice is located in a remote station (left side) and the master in a local station (right side).
II. BACKGROUND Nowadays, haptic devices have found an important usefulness in systems which transfer motor skills such as writing skills [15] and surgical skills [5][6]. Such systems have been use different control approaches to transmit the haptic information, Parra-Vega et. al. [7] provided a guided force feedback to improve and train an operator using a PD Sliding force control, the force is generated based on potential fields which can be tune base on the disability of the operator; Bluteau et. al. [8] uses three haptic guidance techniques, a control guidance in position, in force or without haptic guidance, results shows a global superiority of the second technique over the others two. Some other studies show the advantages of different control strategies, for instance Feygin et. al. [4] studied how subjects learn when they were guided kinesthetically through an ideal complex 3D pattern and it was found that haptic guidance is effective in training. Although, these studies have shown the benefits of visuohaptic guidance techniques, we believe that new strategies that encourage an active participation of the apprentice, without controlling directly his movements, are needed. Various research works have proposed the use of a virtual teacher with a ”record and play” strategy to transfer motor skills, for instance Teo et. al. [9] introduced a virtual teaching system for Chinese ideograms that guides user movement in
TABLE I PARAMETERS OF A FAST ROBOT ARM . Link
Weight
Lenght
Inertia
1
8Kg
0.4m
0.02Kgm2
2
5Kg
0.3m
0.16Kgm2
different levels to optimize the learning or Henmi et. al. [10] whom developed a system for teaching Japanese calligraphy. Both found an improvement in the learning process using the virtual teacher. Yokokohji et. al. [11] proposed a new strategy for providing assistance using haptics in a virtual environment when training for a motor skill and discussed the possibility of using expert strategies to transmit skills. More recently, Palluel-Germain et. al. [12] introduced a visuo-haptic interface to teach children how to write cursive letters by changing the distance between successive points of a discrete trajectory, results show an improvement after the training control sessions. Gillespie et. al. [13] developed a virtual teacher based on a proportional derivative position controller to teach how to move a simulated crane; however it was not found any evidence that guarantee subjects’ improvement because of the virtual teacher. It was claimed that inconclusive results were due to teacher incapacity to choose an appropriate practicing strategy depending on apprentice initial abilities. In the present works, a real instructor teaches new movements to a remote/local apprentice in real time by means of visual and haptic feedback. The visuo-haptic method allows the teacher to create online an effective practicing strategy base on the apprentice initial abilities and help the apprentice with any setback in real time. III. SYSTEM SETUP Fig. 1 shows the remote and local station of the haptic teleoperated training system. It has a 2-DOF robot (see parameters in Table I) whose end-effector is used by the master to draw the desired pattern and a haptic device [14] which is manage by the apprentice to draw the desired path. The Phantom has low inertia and friction and also gravity compensation. The 2-DOF planar manipulator is a fast robot driven by direct-drive AC servomotors with 2048 pulse integrated encoders; each servomotor is controlled by its own servopack which are programmed in torque mode. Both robotic systems are coupled visually, on one hand the master visual interface displays the master drawing path performance, the geometric limits enclosing the drawing path and the apprentice’s drawing movements (Fig. 2), on the other hand the apprentice visual interface displays the desired drawing path and the apprentice’s drawing movements. The planar robot control runs under Debian/RTlinux operating system, while Phantom 1.0 under Linux/RTAI. Both systems are connected to a high speed intranet network without any delay. An XGraph application runs in real time to create the graphic master-apprentice interfaces, these interfaces display the spatial locations of the master, apprentice and geometric
916
limits. The interface changes the trajectory graphs color depending on the temporal state of the master and apprentice. The description of the two visual interfaces is given below.
Fig. 2. Virtual interface of the haptic teleoperated system, a) Master visual interface and b) Apprentice visual interface.
A. Master visual interface It displays the changing master path motion (white color), the geometric limits created in real time enclosing the master path (orange color) and the changing apprentice path motion (blue color). The master path graph changes its normal color according to the distance between the master and apprentice. It changes to red when the master is 6cm ahead and indicates to the master to slow down his velocity and wait for the apprentice. See Fig. 2a.
displays the path being performed by the master and the apprentice tries to follow and maintain the same position and velocity. Five seconds after the master has started the drawing pattern, his previous drawing path starts to be canceled from the display, in such a way that the apprentice is forced to speed up his velocity and keep his concentration on the task. When spatial deviation from the desired path is large enough, the master constraints the apprentice motion kinesthetically. His/her motion is constraint due to the creation of upper and lower boundaries parallel to the master path. In this way, as the apprentice’s motor skill improves the master influence decreases and the upper and lower boundaries get farther from the master path. The next section explains in detail how the boundaries are created. A. Movement Constraints While the master generates on line the desired trajectory, boundaries are created in both sides and parallel to the master path, at a given distance. These boundaries limit the movement of the apprentice and constitute the holonomic constraints of the haptic device. To obtain the functions that describe the boundaries we use an online interpolation of sampled data from the master actual trajectory and we employ a linear interpolation which results in straight lines between each pair of points with discontinuous derivatives. Moreover, since the master and apprentice training task is at low velocity (around 0.5 mm/s) we choose a trajectory sampling rate at 2Hz. As a result of the adopted sampling rate and resolution, the human does not overshoot nor oscillate because the zooming scale is so large that the boundaries curves can be considered as smooth.
B. Apprentice visual interface It displays the changing master desired path motion (white color) and the apprentice path motion (blue color). The apprentice trajectory graph changes to red color when he is 2cm in advance with respect the master path motion, and indicates to the apprentice to slow down his velocity and wait for the master. See Fig. 2b. IV. ACTIVE HAPTIC TRAINING To acquire motor skills, a lot of training is required to be able to learn the temporal and spatial attributes of the task. Nowadays, modern tasks are more involved and more challenged therefore a method able to achieve an active and effective learning is required. We propose an active haptic training method were the apprentice learn the position and velocity patterns of the desired trajectory by her/him self without much intervention of the master. The apprentice learns temporal information such as position and velocity of the desired path using visual feedback. A visual interface
Fig. 3. Example of a complex 2D trajectory and the creation of upper and lower boundaries using a linear interpolation in real time. d is the perpendicular distance between the upper and lower boundaries.
To explain the linear interpolation in real time, consider a pair of known points P0 (x0 , y0 ), P1 (x1 , y1 ) which are interpolated using a linear polynomial. The polynomial has the following structure: 1 [(tf − t)P0 + (t − ti )P1 ] (1) (tf − ti ) where ti is the initial time, tf is the final time and t the current time. Using (1) we construct lines in any interval
917
P (t) =
of time and their geometries depend on the location of the departure and arrival points. Fig. 3 shows the linear interpolation of a curved path and the obtained lower and upper boundaries with 2.5mm of perpendicular distance between them. It is important to mention that because of the linear interpolation is made in real time; the apprentice must start the training task 0.5 seconds later (after the first sample period). During this period the algorithm gets the first pair of points which are interpolated in the second sample period. These 5 seconds wait does not represent any delay in the training task performance because the training duration is much longer than 5 seconds.
VI. EXPERIMENTS To investigate the efficiency of the haptic teleoperation system that actively transfer motor skills, 12 children between 5 and 7 years old were required to learn a 2D drawing path using the end-effector of the Phantom (See Fig. 2). The desired drawing path was performed by the master (drawing skilled subjects of 27 years old) using the end-effector of a planar robot. Master and children were located in different rooms and were introduce before start the experiment. Experiment performance was in real time and children were encourage to perform the task as fast and accurately as possible, having the tracing of the 2D path as a first priority.
V. CONTROL DESIGN The control scheme of the training teleoperated system is shown in Fig. 4. The master robot has always free motion and within this regime the master may not feel any opposing force when he/she creates the desired trajectory. Thus, the joint torque is τ =0
(2)
where τ ∈
