Point-cloud-based Model-mediated Teleoperation

Transcription

1 Point-cloud-based Model-mediated Teleoperation Xiao Xu Institute for Media Technolog Technische Universität München Burak Cimeci Institute for Media Technolog Technische Universität München Eckehard Steinbach Institute for Media Technolog Technische Universität München Abstract In this paper, we etend the concept of modelmediated teleoperation (MMT) to si degrees-of-freedom in comple environments using a time-of-flight (ToF) camera. Compared to the original MMT method, the remote environment is no longer approimated b a simple planar surface, but b a point cloud model. Thus, object surfaces with comple geometr can be used in MMT. In our proposed sstem, the point cloud model is captured b the ToF camera with high temporal resolution (up to 160fps) and a fleible work range (10cm to 5m). Updating the model of the remote environment while the robot is in operation is thus easier compared to the original MMT approach. The point cloud model is transmitted from the teleoperator to the operator using a lossless H.264 codec. In addition, a simple pointcloud-based haptic rendering algorithm is adopted to generate the force feedback signal directl from the point cloud model without first converting it into polgons. Moreover, to compensate for the estimation error of the point cloud model, adaptive position and force control schemes are applied to enable stable and transparent teleoperation. Our eperiments demonstrate the feasibilit and benefits of utiliing the proposed method in MMT. I. INTRODUCTION A tpical teleoperation sstem consists of a master/operator sstem, a slave/teleoperator sstem and a communication link in between [1]. The slave sstem is controlled b position/velocit commands generated b the user s operation on the master sstem, while the haptic information sensed b the slave sstem is returned to and displaed on the master sstem. The user can thus remotel interact with the environment on the slave side. For teleoperation with geographicall separated master and slave sstems, communication dela is unavoidable. Even a small time dela in the haptic channel jeopardies the sstem stabilit and performance [2]. Several control architectures have been developed to enable stable teleoperation in the presence of communication delas. The classical control schemes, however, result in either poor transparenc or poor stabilit properties [3], [4]. In [5], [6], the so-called predictive displa method is developed to address both issues. For this method, a computer graphics (CG) model of the robot arm is overlaed on the real video images, which enables the user to locall view the motion of the slave robot before it actuall moves and hence avoid possible collisions. An etension work of the predictive displa for environment modeling is implemented in [7], where a stereo camera is emploed to capture the remote environment with a pre-scan procedure, and the 3D virtual environment (VE) can be reconstructed accordingl with polgons or meshes. After that, a model of the telerobot is placed in the VE and the user can thus locall interact with the VE without dela. Although the predictive displa method shows advantages compared to the classical control methods, the construction of the VE model is time consuming. In addition, the updating of the environment is not online and becomes even impossible while the robot is in operation. Moreover, the reconstructed virtual environment is tpicall etensive while most areas of it are of no concern to the users during the operation. Different from the methods above, the concept of Modelmediated teleoperation (MMT) is proposed in [8], [9]. In the MMT method, the user is onl concerned with the object and its model that the slave is interacting with. A simple object model (e.g. a plane) is computed based on the position and force signals on the slave side and transmitted back to the master side. The haptic feedback is generated locall based on the received object model. Thus, a stable and transparent teleoperation sstem is guaranteed. During operation, the object model is updated and transmitted back to the master side whenever the slave obtains a new model. Therefore, the pre-scanning of the remote environment is no longer necessar and the estimated object model is adaptivel updated according to changes in the environment. The main challenge for MMT is to build a geometric model of the object in the remote environment and to estimate its phsical properties (impedance). In [10], a damper-spring model is emploed to approimate the environment. In [11], a distance sensor is used to predict the position of a planar surface even before the slave is in contact with it. Both estimation approaches, however, work onl for one degree-of-freedom (DoF) sstem. A 6-DoF estimation method is proposed in [12], [13], et the communication dela and the surface friction are ignored. An approach for estimating the object model in multi-dof with more phsical properties (such as friction coefficient) in the presence of communication delas is proposed in [9], where a 2D planar surface model is etracted from a point cloud that is captured b a stereo camera. The current work on MMT can onl estimate the environment with a rigid planar surface in one of two dimensions. However, in most cases the environment is not a simple plane. A planar approimation of the remote environment leads to large deviations from the real environment and thus results in frequent model updates and incorrect haptic rendering, which degrades the sstem transparenc and even jeopardies the sstem stabilit [14]. Therefore, etending MMT for objects with comple geometr and phsical properties is necessar. In this paper, we propose a point-cloud-based MMT sstem, which works in 3D space with comple environments. Different from previous MMT, the remote environment is neither a simple planar surface nor a simple geometric shape

2 but rather a point cloud model, which can represent an object surface with arbitrar geometric properties. In our work, a time-of-flight (ToF) camera is emploed to capture depth images of the object surface. Due to the high frame rate and fleible work range of the camera, the point cloud model can be obtained ver quickl and the online updating of the environment model is possible while the robot is in operation. In order to get precise point cloud data in 3D, pre-filtering and camera calibration techniques are emploed. The transmission of the point cloud model is based on a H.264 codec running in lossless intra mode, since an information loss in the point cloud model results in force-feedback rendering errors. Moreover, a simple point-cloud-based rendering algorithm [16] is adopted to generate haptic signals on the master side. Finall, a combination of the position and force control methods compensates for the estimation error of the model position. The rest of the paper is organied as follows. Sec. 2 eplains the proposed point-cloud-based approach for modelmediated teleoperation. Sec. 3 shows the results of the eperimental evaluation and discusses potential etensions of our sstem. Sec. 4 concludes this paper and outlines future work. II. POINT-CLOUD-BASED MODEL-MEDIATED TELEOPERATION Fig. 1 shows the idea of MMT, which uses the sensor information on the slave side (position, force/torque, etc.) to build a virtual model of the remote environment, including geometric and phsical properties (model parameters). The model parameters are transmitted to the master side where a local virtual model is reconstructed accordingl. While the user interacts with the remote environment, the haptic feedback is generated locall without an dela based on this virtual model. If the model parameters are perfectl estimated b the sensors on the slave side, the teleoperation sstem can be thus both stable and transparent for arbitrar communication dela. Generall, the main challenges of MMT lie in two aspects: to obtain a precise object model even for comple geometr and to estimate the corresponding phsical properties. In the following, we address the first challenge b developing a pointcloud-based MMT sstem which does not require pre-scanning of the remote environment. The 3D point cloud model is built with the help of a ToF camera (Argos R 3D-P100), which has high frame rate (up to 160fps) and a more fleible work range (10cm to 5m) compared to other 3D cameras, such as Microsoft Kinect and ASUS Xtion (about 50cm to 3m). Operator /Master Force Position /Force Local Model Model Parameters Network Local Model Force Sensor data Position /Force Teleoperator / Fig. 1: Overview of a MMT sstem (adopted from [8]). Master sstem Pm Pm / fm Point cloud reconstruction fm Force Rendering Coordinate Transf. Decoder network P'm / f'm Point cloud modeling Encoder Ps / fs Coordinate Transf. Prefilters Depth images sstem Fig. 2: Overview of the point-cloud-based MMT sstem, where p m, f m, p s and f s are the master position, master force, slave position and slave force, respectivel. A. Sstem overview An overview of the proposed sstem is shown in Fig. 2. The depth images captured b the ToF camera and the slave position and force signals are necessar for estimating the point cloud model. Once the point cloud model is obtained, it is transmitted to the master side. As the data sie of the point cloud model is large, a compression scheme (codec) is emploed to reduce the data sie. On the master side, the reverse processes are implemented and the 3D point cloud model is reconstructed accordingl. Thus, the force-feedback signals can be generated locall based on the point cloud model. B. Pre-filtering block The raw point cloud data (depth images) captured b the 3D camera are normall quite nois, sometimes even with holes due to an invalid work range or wrong reflection (see Fig. 3). Therefore, the raw point cloud data need to be filtered. In this paper, in order to reduce the computational compleit for the online modeling, simple standard filters are emploed. Firstl, a 5 b 5 median filter is applied on each depth image to remove invalid points. Then a temporal average filter for ever 25 frames is emploed to reduce the noise of the depth image. In addition, a fast image inpainting algorithm as described in [15] is applied to fill holes in the depth image. In this hole-filling algorithm, we regard the depth image as a grascale image. Firstl, the hole regions in the depth image are etracted and marked. Afterwards, based on their neighborhoods a isotropic diffusion (convolution with matrices A and B) is applied inside the hole regions for several rounds. The diffusion kernels suggested b [15] are as follows: A = ( a b a b 0 b a b a ) and B = ( c c c c 0 c c c c where a = , b = and c = After filtering, a low-noise depth image is obtained without holes (see Fig. 3). C. Coordinate transformation In order to build the 3D point cloud model in world coordinates from the depth images, a coordinate transformation (back projection) technique is emploed. As illustrated in Fig. 4, three coordinate transformation steps are necessar: 1) from image coordinates to camera coordinates, 2) from camera )

3 holes v u Object in piel coordinates (u v) Object in camera coordinates (c c c) O O c focal length f c Image plane Fig. 3: A depth image before filtering (left) and after filtering (right). The holes are filled b the median, average and inpainting filters. coordinates to robot tool coordinates (R 1 and t 1 ) and 3) from robot tool coordinates to world coordinates (R 2 and t 2 ). In the first step, ever point in the depth image can be described as (u,v,) T, where u and v are the piel coordinates in rows and columns and is the distance value. The purpose of this step is to transform (u,v,) T to camera coordinates that are described b ( c, c, c ) T. We assume that the camera view can be modeled b an ideal pinhole camera model which projects 3D points onto a 2D image plane (Fig. 5). Therefore, the transformation from image coordinates (u,v,) T to camera coordinates ( c, c, c ) T is as follows: c = (o v) c / f, c = (u o ) c / f, c = where f and f are the camera focal lengths in and directions, o and o are the piel shifts from the camera center. For the second and third steps, the transformation from ( c, c, c ) T to robot tool coordinates ( t, t, t ) T and then to world coordinates ( w, w, w ) T requires both rotation and translation: ( w, w, w ) T = R 2 ( t, t, t ) T + t 2 = R 2 R 1 ( c, c, c ) T + (R 2 t 1 + t 2 ) So far the 3D object point cloud model in the world coordinate sstem is obtained. The net step is to estimate the model properties and render haptic signals based on the point cloud model. Ow R2, t2 Ot Oc R1, t1 3D camera Fig. 4: Overview of the coordinate transformation in the proposed sstem. Fig. 5: Back-projection from image to camera coordinates. D. Modeling block In this paper, we consider the object as a static rigid bod without friction. Therefore, the onl model parameter that needs to be estimated is the geometr of the object, which is represented b the 3D point cloud. As we assume rigid objects, the stiffness is set to be the maimum value that the master device can displa. E. Force rendering The function of this block is to render haptic signals directl based on the 3D point cloud. A similar method as [16] with a fast plane detection algorithm is emploed. As illustrated in Fig. 6, the haptic interaction point (HIP) and pro are used to detect collision and render the force. If the HIP is outside of the estimated surface, the pro follows the motion of the HIP. If there are an points within r 1, the pro is entrenched and it is moved one step in the direction of n. If there are an points between r 1 and r 2 and the HIP is inside the estimated surface, the pro is considered to be in contact with the object. Thus, the motion of the pro is constrained on the estimated surface (in the direction of v), and a temporar plane model is estimated using all the points p i = ( i, i, i ) T,i I between r 1 and r 2. Similar to [17], the plane center is set to be the average position of all the points and the normal vector can be obtained b the eigen-analsis of the covariance matri C R 3 3 for all p i, where the matri C is calculated as: C = 1 I I i=1,i I p i p T i Assume that λ 1,λ 2,λ 3 are the 3 eigenvalues of the covariance matri C and v 1,v 2,v 3 are the corresponding eigenvectors. Thus, the plane normal n is obtained b the eigenvector which corresponds to the minimum eigenvalue: n = v k, k = arg min k {1,2,3} {λ 1,λ 2,λ 3 } Once the temporar plane normal is obtained, the haptic signal can be rendered at 1kH with a simple spring model based on Hooke s law. F. Codec According to the MMT method, the estimated point cloud model is transmitted to the master side. In our sstem, rather than transmitting the 3D point cloud data directl, we transmit the coordinate rotation and translation parameters along with the filtered depth image. On the master side the same

4 r2 r1 pro HIP f Normal vector n Fig. 6: The definition of the pro (left) and the estimation of the surface normal (right). v holes ToF Camera Omega.6 Ow trajector (a) JR3 Force Sensor trajector Fig. 8: (a) Eperimental setup and (b) the reconstructed 3D point cloud model for the steel semispherical shell. (b) p A p s0 Real object position Estimated object position (a) p A Master Δ p s0 Δ p m Position after shift v s Real object position Master Position after shift (b) v m Estimated object position Δ ' Fig. 7: Model shifts for the error cases of the position estimation. (a) The slave is in contact with the environment before the master, thus the estimated object model is shifted along. While the master is leaving the object model, the model is shifted along. (b) The master is in contact with the object model before the slave, the model is thus shifted b a large displacement, which changes the situation to case 1. coordinate transformation is applied and the 3D point cloud model can be thus reconstructed. Although the sie of the depth image is much smaller than the 3D point cloud, a compression scheme is needed to further reduce the data sie. As alread small compression errors result in large deviation of the reconstructed 3D point cloud model on the master side, a lossless compression scheme is required. In our work, we consider the depth image as a grascale image and a lossless H.264 compression scheme running in intra mode (with GOP structure of onl I frames) is emploed to achieve this aim. G. Model update While the robot is in free space, it is controlled using position control. The point cloud model is updated and transmitted to the master side twice ever second (2H). Since the object in the remote environment is supposed to be static, a higher update rate is not necessar. Once the contact occurs on the slave side in one or more coordinate directions: fs > f thres and/or fs > f thres and/or fs > f thres ( f s {,,} > f thres ), the robot switches to force control in the corresponding direction. To enable a stable teleoperation without an model-jump effect [14], the updating of the point cloud model is thus stopped. However, due to the estimation error of the 3D point cloud, there will be small position differences between the real object and the estimated point cloud model, which results in the following three cases: 1) The slave is in contact with the object ( f {,,} s > f thres ) while the master HIP is still in free space ( f m 0); 2) The slave is in free space ( f s {,,} > f thres ) while the contact occurs on the master side ( f m > 0); 3) The slave is contact with the object ( f s {,,} > f thres ) at the same time instant when the contact occurs on the master side ( f m > 0), but the position estimation of the object model is still wrong. For the cases 1 and 3, the estimated point cloud model is displaced to compensate for the estimation errors (Fig. 7). The displacement vector is computed as follows According to the invariant relative position between the slave end effector and the ToF camera, the position of the slave end effector in the camera coordinates is fied. Thus, the real contact position p s0 (the position of the slave end effector) and the estimated contact position p A in the point cloud model can be computed; If the position of p A is identical to the current slave end effector position p s0, the model is correctl estimated. Otherwise, p A and p s0 are transmitted back to the master side and the displacement vector is computed as = p m p A, where p m is the master HIP position; While the master HIP is leaving the object model, the model is shifted along the vector = p s0 p m, with the restriction that the model surface is alwas just below the master HIP until it reaches the correct position p s0. For case 2, the point cloud model is shifted along the direction of the current master velocit b a large displacement in order to dela the haptic contact on the master side (Fig. 7(b)), which changes the situation to case 1 and hence the procedures described above are applied. III. EXPERIMENTAL RESULTS In this section, an eperiment is conducted to evaluate the performance of our proposed sstem in a real teleoperation sstem with a comple remote environment. A. Setup The sstem setup is shown in Fig. 8(a). A Force Dimension R Omega.6 and a KUKA LWR arm are used as the master and slave devices, respectivel. A JR3 force sensor is equipped on the slave robot to measure the slave force f s. The Argos R 3D-P100 ToF camera is used to capture the depth images. The software environment is based on ROS ( and the SDK of Force Dimension.

5 (a) (b) (c) (d) (e) (f) (g) Fig. 9: Eperimental results. (a)-(c) the master and slave position in, and directions, respectivel. (d)-(f) the master and slave force in,, and directions, respectivel. (g) the data rate vs. time. B. Eperiemental design To test the sstem performance, a semispherical steel shell is place on the slave side as a comple rigid object. A smooth paper tape is pasted on the shell to reduce the friction (see Fig. 8(a)). The estimated point cloud model and the trajector of the master HIP are illustrated in Fig. 8(b). The frame rate of the ToF camera is set to 50fps. Due to the 2H update rate of the point cloud model, a temporal average of ever 25 frames is computed as the depth image inputs of our sstem. In addition, the rotation and translation for the coordinate transformation (Fig. 4) are obtained b the camera calibration (R 1,t 1 ) and the robot status in 3D space (R 2,t 2 ). The gap between the pro radius r 1 and r 2 in Fig. 6 is chosen to be 5mm, which is just larger than the noise level of the point cloud captured b the ToF camera. The force threshold f thres is set to be 0.5N b considering the noise of the JR3 sensor. During the eperiment, the forward and backward communication delas are set to be a fied value T f = T b = T d =500ms. C. Eperiemental results Figs. 9(a)-(f) show the position and force signals on both master and slave sides. From 0s to about 3.5s (point A to B in Fig. 9(f)), both slave and master HIP are in free space. Thus, the master force f m is ero and the slave force f s is nearl ero (due to the measuring noise of the JR3 force sensor). At the time instant of about 3.5s (point B), the robot is in contact with the object and switches to the force control mode. If the object model is correctl estimated, the contact on the master side should occur at about 3s (T d = 500ms). However, the estimated model position has a small error in -direction, which can be observed in Fig. 9(c) for the point a (the contact position on the slave side) and the point b (the contact position on the master side). Therefore, the master is still in the free space until about 3.4s (point b in Fig. 9(c)) while the slave is keeping in contact with the environment and waiting for the command sent b the master (point B to C). From about 3.5s to 7s, the master HIP is in contact with the estimated object model and moves on the model surface according to the trajector designed in Fig. 8(b), and the slave follows the master s motion/force with a dela of 500ms (point C to D). From point D to point E, the slave senses a force impulse due to a small pressure on the master side, which can be observed in the master position signals in and directions between about 7s to 7.5s (Fig. 9(b)(c)). After about 8.5s (point E), the slave starts to leave the object and thus the measured slave force reduces to ero (with small noise). During the contact, the mean force errors in, and direction are 0.33N, 0.43N and 0.28N with the standard deviation of 0.14N, 0.22N and 0.05N, respectivel. D. Results of the data transmission The data rate as a function of time is shown in Fig. 8(g). From 0s to about 3s and after 9s, the slave is in free space. Thus, the estimated object model is updated at a rate of 2H. According to Fig. 9(g), the average data sie of each depth image frame is about 3.8kBte (including the rotation and translation parameters for the coordinate transformation). Therefore, the data rate in the communication channel is computed as r = 3.8kB 2H = 7.4kB/s. The maimal encoding time (including prefiltering) for all the frames are less than 5ms, which is negligible compared to the communication dela and thus meets the demands of the computational time for the real-time coding. During the slave s contact with the object, the updating of the point cloud model is stopped. However, the data rate is still not completel ero since parameters such as the slave contact position p s0 and the estimated contact position p A in the point cloud model are needed for computing the displacement vectors on the master side (Fig. 7). E. Discussion 1) Noise reduction: As discussed in Sec. 2, a small modeling error due to the noise of the depth image results in

6 a force mismatch between the slave and master. Although a control scheme is emploed to compensate for this error, with increasing delas the shifting of the object model leads to unrealistic eperience during the interaction [18]. Therefore, additional filters and distance sensors with higher resolution are needed to further reduce the estimation errors. 2) Estimation of the phsical properties: In this paper we onl consider the object as a static rigid bod. However, for comple environments more phsical properties (such as softness and friction) should be included. To estimate these properties, an online estimation algorithm should be developed with inputs of the slave position, force and the point cloud model. Additional sensors could also be applied to remotel measure the object properties even before the slave is in contact with the environment. 3) Model update: In our sstem, the point cloud model is no longer updated while the slave is in contact with the object. However, both object geometr and phsical properties could change over time. Therefore, a new updating scheme should be applied on the slave side to manage when and how to update the point cloud model while the slave is in contact with the object. A simple wa to trigger the updates is to use the perceptual deadband method proposed in [19]: if the force difference between the slave and master is larger than a threshold, an update is triggered (Fig. 10). The updating of the object model can be for both geometric and phsical properties of the entire or partial point cloud model. network P'm / f'm Point cloud modeling fs Updating f'm controller Encoder Ps / fs Coordinate Transf. Prefilter Depth image sstem Fig. 10: The modified sstem structure for the model updating. IV. CONCLUSION In this paper, we propose a point-cloud-based modelmediated teleoperation (MMT) sstem to etend the current MMT approach to comple environments in si DoF. In our sstem, the environment model is no longer approimated b a simple planar surface, but b point cloud. A ToF camera is emploed to capture the depth images of the environment at high frame rate. Due to the fleible work range of the ToF camera, the updating of the point cloud model is available even when the slave is close to the object, which enables a stable and precise modeling of the object geometric properties. Function blocks such as pre-filtering, coordinate transformation, haptic rendering and depth image codec are developed accordingl. In addition, the use of the position/force control scheme compensates for the estimation error of the model position due to the noise of the depth image measuring. In future work, the potential etensions that are discussed in Sec. 3 will be implemented. In addition, we plan to develop the modeling algorithm for more comple environment such as soft and deformable objects. Moreover, subjective eperiments will be conducted to evaluate both the subjective eperience and the objective task performance of the proposed sstem. ACKNOWLEDGMENT This work has been supported b the European Research Council under the European Unions Seventh Framework Programme (FP7/ ) / ERC Grant agreement no The authors would like to thank Nicolas Alt, Clemens Schuwerk, Rahul Chaudhari and Anas Al-Nuaimi for their technical support on this paper. REFERENCES [1] W. Ferrell and T. Sheridan. Supervisor control of remote manipulation. IEEE Spectrum, vol. 4, no. 10, pp , [2] D. Lawrence. Stabilit and transparenc in bilateral teleoperation. IEEE Transactions on Robotics and Automation, vol. 9, pp , [3] G. Niemeer and J.-J. Slotine. Stable Adaptive Teleoperation. IEEE Journal of Oceanic Engineering, vol. 16, pp , [4] R. Daniel and P. McAree, Fundamental limits of performance for force reflecting teleoperation. The Int. J. of Robotics Research, vol. 17, no. 8, pp , [5] A. Bejc, W. Kim and S. Venema. The phantom robot: predictive displas for teleoperation with time dela. In Proceeding of the IEEE international conference on robotics and automation, [6] A. Bejc and W. Kim. Predictive displas and shared compliance control for time-delaed telemanipulation. In Proceeding of the international conference on IROS, Ibaraki, Japan, [7] Tim Burkert, Jan Leupold and Georg Passig. A Photo-Realistic Predictive Displa. Presence: Teleoperators and Virtual Environments, vol. 13, no. 1, pp , [8] P. Mitra and G. Niemeer. Model mediated telemanipulation. International Journal of Robotics Research, vol. 27, no. 2, pp , [9] B. Willaert, J. Bohg, H. Brussel and G. Niemeer. Towards multi-dof model mediated teleoperation: using vision to augment feedback. IEEE International Workshop on HAVE, [10] H. Li and A. Song. Virtual-Environment Modeling and Correction for Force-Reflecting Teleoperation With Time Dela. IEEE Transactions on Industrial Electronics, vol. 54, no. 2, pp , [11] Farid Mobasser and Kevan Hashtrudi-Zaad. Predictive Teleoperation using Laser Rangefinder. In Proceeding of CCECE, [12] X. Xu, J. Kammerl, R. Chaudhari and E. Steinbach. Hbrid signalbased and geometr-based prediction for haptic data reduction. IEEE International Workshop on HAVE, [13] A. Achhammer, C. Weber, A. Peer and M. Buss. Improvement of model-mediated teleoperation using a new hbrid environment estimation technique. In Proc. of the international conference on Robotics and Automation, [14] B. Willaert, H. Brussel and G. Niemeer. Stabilit of Model-Mediated Teleoperation: Discussion and Eperiments. Eurohaptics, [15] Manuel M. Oliveira, Brian Bowen, Richard McKenna and Yu-sung Chang. Fast Digital Image Inpainting. Proc. of the international conference on VIIP, Marbella, Spain, [16] Fredrik Rden, Sina Nia Kosari and Howard Ja Chieck. Pro Method for Fast Haptic Rendering from Time Varing Point Clouds. Proc. of 2011 IEEE/RSJ international conference on Intelligent Robots and Sstems, San Francisco, CA, USA, [17] Poppinga, J., N. Vaskevicius, A. Birk and K. Pathak. Fast plane detection and polgonaliation in nois 3d range images. International Conference on Intelligent Robots and Sstems, Nice, France, [18] X. Xu, G. Paggetti and E. Steinbach, Dnamic Model Displacement for Model-mediated Teleoperation. IEEE World Haptics Conference, Daejeon, Korea, [19] P. Hinterseer, E. Steinbach, S. Hirche and M. Buss. A novel, pschophsicall motivated transmission approach for haptic data streams in telepresence and teleaction sstems. Proc. of ICASSP, Philadelphia, PA, USA, 2005.