The Essential Components of Human-Friendly. Australian National University. URL:

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1 The Essential Components of Human-Friendly Robot Systems Y. Matsumoto, J. Heinzmann and A.Zelinsky Research School of Information Sciences and Engineering Australian National University Canberra, ACT 0200, Australia URL: Abstract To develop human friendly robots we required two key components; visual interfaces and safe mechanisms. Visual interfaces facilitate natural and easy interfaces for human-robot interaction. Facial gestures can be a natural way to control a robot. In this paper, we report on a vision-based interface that in real-time tracks a user's facial features and gaze point. Human friendly robots must also have high integrity safety systems that ensure that people are never harmed. To guarantee human safety we require manipulator mechanisms in which all actuators are force controlled in a manner that prevents dangerous impacts with people and the environment. In this paper we report on a control scheme for the Barrett-MIT whole arm manipulator (WAM) which allows people to safely interact with the robot. 1 Introduction If robotics technology is to be introduced into the everyday human world, the technology must not only operate eciently executing complex tasks such house cleaning or putting out the garbage, the technology must be safe and easy for people to use. What constitutes a human friendly robot? Firstly, human friendly robots must be easy to use and have natural communication interfaces. People naturally express themselves through language, facial gestures and expressions. Speech recognition in controlled situations using a limited vocabulary with minimal background noise interference is now possible and could be included in human-robot interfaces. However, visionbased human-computer interfaces are only recently began to attract considerable attention [1, 2, 3]. With a visual interface a robot could recognise facial gestures Graduate School of Information Science, Nara Institute of Science and Technology, Takayamacho, Ikoma-city, Nara, , Japan such as"yes" or "no", as well being able to determine the users gaze point i.e. where the person is looking. The ability to estimate a persons gaze point is most important for a human-friendly robot. For example a robot assisting the disabled may need to pick up items that attract the users gaze. Our goal is to build \smart" visual interfaced for use in human-robot applications. In this paper we describe our most recent results in this area. The second requirement for human-friendly robots is that they must possess a safety system of high integrity that ensures the robots actions never result in physical harm to a human. One way to solve this problem is to build robots that are small, light-weight and slow moving. However, this will result in systems that wont be able to lift or carry objects of any signicance. The only alternative is to build systems that are strong and fast, but which arehuman-friendly. Current commercial robot manipulator technology use point topoint control and should be considered is human-unfriendly. Point topointcontrol is quite dangerous to use in dynamic environments. For example, if an object unexpectedly blocks a planned point to point path, either the object is smashed out of the robots paths or the robot sustaina considerable damage. Clearly, a compliant force based controller is needed. However, the commercial technology for force-based control of robots is not yet readily available. The current practice is to add force sensors, usually at the tool plate to existing robot manipulators. While this allows force control of the robots end point, other parts of the manipulator could still collide with unexpected objects. To ensure safety a Whole Arm Manipulation (WAM) approach is need, where all the robots joints are force controlled. Another important aspect is that robot systems must guard against software failures, and prevent robots from continuing to work. In this paper we describe our recent results to build a safe control architecture for a robot manipulator.

2 2 Visual Interfaces 2.1 Face and Gaze Detection There are several types of commercial products in existence to detect head position and orientation, using magnetic sensors and link mechanisms. There are also several companies supporting products that perform eye gaze tracking. These products are generally highly accurate and reliable. However, all these products require either expensive hardware and/or articial environments (helmets, infrared lighting, marking on the face etc). The restricted motion and discomfort to user caused by such equipment makes it dicult to measure natural and unihibited behavior of people. To solve this problem, there have been many research results reported that are related to the visual detection of head pose [1, 2,3,4, 5, 6, 7]. Recent advances in computer hardware have allowed researchers to develop real-time face tracking systems. However all of the previously reported systems are based on monocular vision. Recovering the 3D pose from a monocular image stream is regarded as a dicult problem in general. High accuracy as well as robustness are particularly hard to achieve. Most reported systems do not compute the full 3D 6DOF posture of heads. Researchers have developed monocular systems to detect both the head pose and gaze point simultaneously [8, 9], However, these systems do not accurately determine the 3D vector of the gaze direction. In order to construct a system which observes a person without causing any discomfort, the system should satisfy the following requirements: non-contact passive real-time robust to occlusions and lighting change compact accurate capable to detect head posture and a gaze direction simultaneously Our system simultaneously satises all the listed requirements by utilizing the following techniques: stereo vision using eld multiplexing image processing using normalized correlation 3D model tting using virtual springs 3 Real-time Vision Hardware Figure 1 illustrates the hardware setup of our realtime stereo face tracking system. It has a NTSC camera pair (SONY EVI-370DG 2) to capture the person's face. The output video signals from the cameras are multiplexed into one video signal using the \eld Stereo Camera MUX Monitor IP5000 Vision Processor PentiumII 450MHz 64MB Memory PC SGI O2 Figure 1: System conguration of the human-machine interface. multiplexing technique"[10]. The multiplexed video stream is then fed into a vision processing board (Hitach IP5000), where the position and the orientation of the face are determined. The face tracking results are visualized on a SGI O2 graphics workstation. 3.1 Hitachi IP5000 Image Processor The Hitachi IP5000 is a PCI half-sized image processing board is used in this research. The card is equipped with 40 frame memories of pixels. It provides in hardware a wide variety of fast image processing functions such as binarization, convolution, ltering, labeling, histogram calculation, color extraction and normalized correlation. The frequency of these operations is 73.5MHz, which means the card can apply a single basic function, such as binarization) to a single image in 3.6ms. 3.2 Field Multiplexing Device The eld multiplexing is a method for generating an analog multiplexed video steam from two video streams. A diagram of the device is shown in Figure 2. The device takes two video steams which are synchronized into a video switching IC. The video switcher selects one signal and uses it as odd eld of the video output, the other signal becomes the even eld of video output. Since the frequency of the switching is only 60Hz, the multiplexer can be easily and cheaply implemented using only consumer electronic parts. A photo of the device is also shown in Figure 2, its size is less than 5cm square. The advantage of multiplexing video signals in the analog phase is that such an approach can be applied to any vision system. Single video stream processing is transformed into stereo vision processing. Since the multiplexed image is stored in a single video frame memory, stereo image processing can be performed with in the memory. This means there is no overhead cost for transferring images which is inevitable in stereo vision system with two image processing boards. Thus a system with a eld multiplexing device can have a higher performance than a system with two

3 boards. A minor weak point of the eld multiplexing is that the image looks strange to human eyes if you display the signal directly on a TV monitor, because two images are superimposed every two lines. However this doesn't make image processing any harder, since a normal image can be easily obtained by subsampling the multiplexed image in the vertical direction. calculated based on stereo matching. In the manual acuisition mode, the image patches of the features are selected by simply clicking with a mouse over positions of interest in the image. Stereo matching is then performed to calculate the 3D coordinates. Field Multiplexing Device Right Camera Left Camera Vin1 (NTSC) SyncOut (NTSC) Vin2 (NTSC) Buffer Sync Separator Video Switch Buffer MixOut (NTSC) Image Processing System Feature templates Feature coordinates (-49, 15, -9) (-17, 15, -5) ( 17, 12, -4) ( 48, 10, -3) (-27,-57, -1) ( 23,-58, 0) Whole face template MixOut Vcc GND Vin1 Vin2 SyncOut Figure 2: Field Multiplexing Device. 4 Stereo Face Tracking 4.1 3D Facial Model The 3D facial model used in our stereo face tracking is composed of three components: template images of the facial features, 3D coordinates of the facial features, an image of the entire face. The facial features are dened as both of the corners of the eyes and the mouth. Thus there are six feature images and coordinates in a facial model, an example of which isshown in Figure 3. The facial model also has an image of the whole face in stored in low resolution. This image is used to search for a face at the system initialisation stage and in cases when the system feature tracking fails. The facial model can be acquired either \automatically" or \manually". In automatic acuisition mode, the eyes and mouth are detected by rst nding skin colored regions in the image and then binarizing the intensity information contained in the skin colored facial region. Small image patches at both ends of the extracted eyes and mouth are memorized as feature templates, and the 3D coordinates of the features are Figure 3: Upper: Extracted tracking features from stereo images, Lower: 3D facial model. 4.2 Stereo Tracking Algorithm The owchart describing the stereo tracking algorithm is shown in Figure 4. Before face tracking starts, the error recovery procedure is executed to determine the approximate position of the face in the live video stream using the whole face image. Feature tracking and stereo matching for each feature is carried out to determine the 3D positions of each feature. A 3D facial model is tted to the 3D measurements, and the 3D position and orientation of the face is estimated in terms of six parameters. Then the 3D coordinates for each feature are adjusted to maintain the consistency of the rigid body facial model. Finally, the 3D feature coordinates are projected back onto the 2D image plane in order to update the search area for feature tracking by the vision processor in the next frame. At the end of each tracking process cycle, the overall reliability of the face tracking is determined using the correlation values of feature tracking and stereo matching. If the reliability is higher than a preset threshold, the system returns to the beginning of the tracking process again. Otherwise the system decides

4 it has lost the face and jumps back to the error recovery phase. Stereo Video Stream right image right+left image right image right+left image 2D feature tracking 2D feature position 3D stereo matching 3D model fitting 2D projection y start 2D face searching 3D stereo matching 2D projection OK? 2D face position 3D face position 2D feature position 3D feature feature 3D face position 2D feature position n Error Recovery Face Tracking Figure 4: Tracking Algorithm D Feature Tracking face image 3D structure feature image 3D structure 3D facial model In the 3D feature tracking stage, it is assumed that each feature has only small displacement between the current frame and the previous one. The 2D position of features in the previous frame are used to determine the search area in the current frame. The feature images stored with the 3D facial model are used as templates. Images from the right camera are searched for features. The 2D features that have been found in the right image are used as templates for searching for matching images from the left camera. By stereo matching the 3D coordinates of each feature are acquired. The processing time of the whole tracking process (i.e. feature tracking + stereo matching for six features) is about 10ms by the IP D Model Fitting Figure 5 illustrates the coordinate system used to represent the position and the orientation of the face. The parameters (; ; ') represents the orientation of the face, and (x,y,z) represents the position of the face center relative to the the origin of the camera axis. The diagrams in Figure 6 describe the model tting scheme used in our system. In the actual implementation six features are used for tracking, however Z Y O Translation X (x,y,z) Rotation (φ,θ,ϕ) Figure 5: Coordinate system for face tracking. only three points are illustrated in the diagrams for simplicity. The basic idea of the model tting is to iteratively move the model closer to the system measurements while considering the reliability results of feature tracking. As mentioned before, the face is assumed to have a small motion between the frames. This means there can be only small displacements in terms of the position and the orientation, which is described as (x,y,z,; ; ') in Figure 6(1). The position and the orientation determined in the previous frame (at time t) are used to rotate and translate the data set of measurements from the vision system to the same coordinate space as the model, as shown in Figure 6(2). After the rotation and translation, the data set of measurements have a small disparity from the model due to the motion which has occurred during the interval t. Fine tting of the model is performed next. To realize a robust tting of the model, it is essential to take the reliability values of the individual measurements into account. The least squares method is usually adopted for such purposes. In our system, a similar tting approach based on virtual springs is used. The result of 3D tracking yields two correlation values (for the left and right images) which are between 0 and 1 for each feature. If a template and another matching region have exactly the same pattern, then the resulting image correlation value is 1. A value of 0 represents correlation where all pixels in the two correlation images are maximally dierent. The product of the two correlation values for each feature can be regarded as a reliability value. The reliability values are used as the parameters of stiness of springs that link each feature in the model. The spring based model iting is shown in Figure 6(3). The model is iteratively rotated and translated in order to reduce the elastic energy of the springs. Using the tracking reliability as a spring constants makes the results of model tting insensitive to the partial matching failures, and ensures robust face tracking. The processing time of the iterative model tting takes less than 2ms using a PentiumII 450MHz processor.

5 (1) Z (2) Z (3) Z k2 Y O model Y F2 O Y O model Pos (0,0,0) Orient (0,0,0) displacement due to ( x, y, z) and ( φ, θ, ϕ) still remains F1 k1 At time model object Pos (x+ x, y+ y, z+ z) y Orient (φ+ φ, θ+ θ, ϕ+ ϕ) x o z Rot ( φ, θ, ϕ) Trans (-x,-y,-z) X X measurement k3 F3 object y o z X t At time t + t Pos (x,y,z) Orient (φ,θ,ϕ) x 3D model fitting based on virtual springs k n Stiffness of spring correlation value The whole face image shown in Figure 3 is used in this process. In order to reduce the processing time, the template is memorized in low resolution. The live video streams is also reduced in resolution. The template is rst searched in the right image, and then the matched image is searched in the left image. As a result, the rough 3D position of the face is determined and is then used as the initial state of the face for the face tracking. This searching process takes about 100ms. 5 Implementation Results 5.1 Face Tracking Some snapshots of the results of the real-time face tracking system are shown Figure 7. Images (1) and (2) in Figure 7 show results when the face rotates, while (3) and (4) show results when the face moves closer to and further from the camera. The whole tracking process takes about 30ms which iswithina NTSC video frame rate. The accuracy of the tracking is approximately 1mm in translation and 1 in rotation. The snapshots in Figure 8 show the results of tracking when there is some deformation of the facial features and partial occlusions of the face by a hand. The results indicate our tracking system works quite robustly in such situations owing to our model tting method. By utilizing the normalized correlation function on the IP5000, the tracking system is tolerant of the uctuation in lighting. (1) (2) Figure 6: 3D model tting algorithm. 4.3 Error Recovery The tracking method described above has only a small search area in the image, which enable the real-time processing and continuous stable result of tracking. However once the system fails to track the face, it is hard for the system to make a recovery by using only the local template matching, and a complementary method for nding the face in the image is necessary as a error recovery function. This process is also used at the beginning of the tracking when the position of the face is unknown. (3) (4) Figure 7: Face tracking with various head poses.

6 (1) (2) (3) (4) Figure 8: Face tracking with feature deformation and occlusion. Figure 9: Visualization of tracking results. 5.2 Visualization The results of the tracking are visualized using a SGI O2 graphics workstation. Figure 9 illustrates examples of the tracking results and the corresponding visualization. The 3D model used in the visualization consists of rigid surface of the face and two eyeballs. The face has six DOF for position and orientation, and the eyeballs have two DOF respectively. The position of the irises of the eyes is detected using the circular hough transform, which are used to move the eyes of the mannequin head. The visualization process is performed online during the tracking, therefore the mannequin head can mimic the person's head and eye motions in real-time. 5.3 Gaze Detection By merging the information on the position and orientation of the face and the position of the pupils of the eyes, the 3D gaze vector can be computed. Figure 10 shows snapshots from a 3D gaze detection experiment. Detecting the position of the irises takes about 2ms and the full face tracking and 3D gaze detection system runs at 20Hz. The goal of this research is to be develop a system that can be used as a visual interface between a human and a robot. However, the system has obvious other uses. It could be applied to measuring human performance in psychological experiments, ergonomic design, as well as products for the disabled and the amusement industry. Figure 10: Gaze detection. 6 Human-friendly robots The goal of this research is to construct capable robots (as opposed to small, intrinsically safe toys) that are able to interact physically with humans in a safe way. Consider the motor vehicle, despite the potential dangers a car poses to it's driver and people in its vicinity, most people feel comfortable and safe while driving cars or walking on the side of streets. The reason for this is that a human driver is able to control the overall behaviour of the car using the steering wheel, brake and throttle. Various electronic and mechanical systems are providing specialised functions such as clutch and gear handling in automatics and anti locking systems. It is of paramount importance that the driver is always in perfect in control of the overall behaviour of technical system, and that he/she can understand and predict how the overall system behaves and how the

7 control inputs relate to this behaviour. This mechanism fails when a driver makes an emergency stop without ABS and the car does not respond to steering actions anymore. It is counterintuitive to release the brakes in an emergency situation to regain steering control. A similar approach to making technical systems understandable is appropriate for the research area of human-friendly robots. It is of major importance that the user is in control of the overall behaviour and is able to predict the system's response. 6.1 Basic Behaviour The basic behaviour of the robot is to compensate for gravity (Zero-G behaviour). In this mode the robot is completely passive and appears to be weightless. A human can easily move the robot around by pushing the links. The motion of the robot is slowed down only by the (low) friction in the joints and when pushed the robot keeps moving until it collides with an obstacle or reaches the joint limits. This behaviour is achieved by feed-forward control only. We consider this mode to be safe in terms of humanfriendly robots. Since the robot is completely passive, all actions of the robot are directly initiated by the operator. Also, the behaviour of the robot can be easily predicted even by non-experts. The robot can only cause harm when abused by the operator. Thus, a passive robot in Zero-G mode is safe in terms of humanrobot interaction. However, autonomous motion initiated by the robot has to be considered potentially dangerous. To ensure safe operation, restrictions on the robots actions have to be guaranteed at all times. The underlying theory to derive appropriate joint torques is well understood and can be found in the literature, see [11]. 6.2 Safe Motion Control By specifying additional torques parallel to the torques required for the gravity compensation the robot can achieve self-controlled motions. This is required for all tasks in which the robot moves by itself. The threat posed by self-controlled motion is the collision of the robot with a human. With todays technology it is not feasible to detect the presence and position of humans in the robots workspace reliably. Collisions of the robot with the operator can not be ruled out, and therefore, the threat posed by such a collision has to be restricted to an acceptable level. 6.3 Impact force The impact force provides a good measure for how dangerous a potential collision with a person could be. The collision between the mechanical system and an obstacle can occur at any point p on the surface of the mechanical system. The impact force ^F of a particular point p on the surface of a serial manipulator with an stationary obstacle can be found using equations derived by Walker et al. [12] ^F =,(1 + e)v t n n t J p ()I,1 ()J t p ()n (1) Here, v 2 IR 3 is the velocity of the mechanical system at the point p of impact, 2 IR n are the joint angles, J p () is the manipulator Jacobian for the point p in the mechanical system where the contact occurs, I() isthenn inertia matrix, and n 2 IR 3 is the unit contact normal vector. In the worst case v is aligned with n but pointing in the opposite direction (v =,s v n);s v = kvk and the impact is elastic (e = 1). In this worst case scenario 1 simplies to: ^F max = = =,2v t (, 1 s v v) (, 1 s v v t )J p ()I,1 ()J t p()(, 1 s v v) (2) 2s v v t v v t J p ()I,1 ()J t p ()v (3) 2s 3 v v t J p ()I,1 ()J t p ()v (4) With v = J() _ and sv = kvk = kj() k, _ ^F max can be expressed as a function of the state (; )ofthe _ mechanical system. ^F max = 2kJ p () _ k 3 _ t J t p()j p ()I,1 ()J t p()j p () _ (5) ^F max is undened for v = J() _ = 0. However, for a stationary point the impact energy with a stationary obstacle equals 0 and ^F max is dened accordingly. 6.4 Formal denition of Impactergy To describe the ability of a robot to cause impact we introduce the term Impactergy. We dene Impactergy to be the maximum impact force that a moving mechanical system can cause with a static environment. In this case we have to consider all points p 2 P on the surface of the robot. This makes ^F max : P IR n IR n 7! IR a function of the point p and the dynamic state (; ) _ of the system. We can now dene the Impactergy of the system as =sup p ; where p = ^F max (p; ; ) _ (6) p2p The Impactergy is a well-dened scalar value that provides a upper limit for any impactofthesystem

8 Dynamics / Space Conv. Robot programming layer Safety Envelope : Limit additional motor torques Velocity Guard disable Zero-G (Grav. Comp.) External Forces (Humans) disable Obstacles Torque Ripple Compensation Safety Hearbeat Figure 11: Safety software architecture for the control of the WAM robot. with a static object. Since it assumes elastic impact and aligned surface normals, the limit is rather conservative. We can now dene a limit max for the impact force which can be considered safe in the event of a collision of the robot with a person. The maximum impactergy we allow for a robot denes the safe region in the state space where the following inequality holds p max 8p 2 P (7) 6.5 Controlling the impact The goal of the novel Impact Control-scheme is to ensure that the robot can not leave the safe region in state space. However, external forces may still force the robot outside the safe region, eg when pushed by the operator. The Impact Controller acts as a saturating lter between the motion control algorithm and the robot (refer to Figure 11). It provides a safety envelope which encapsulates the robot. The impact controller passes the torque vector generated by the motion control algorithm unchanged to the robot as long as it complies with the safety constraints set in the impact controller. If the desired torque vector does not satisfy the safety constraints, a clipping function has to be applied to the torque vector. The resulting safe torque vector guarantees that the robot can not leave the safe region. A suitable state-dependent control constraint is de- ned in the following inequality. d dt p 1 t c ( max, p ) 8p 2 P (8) The time constant t c denes how fast the robot may gain impactergy. It can be set in a way that the motion of the robot is predictable for the operator. The clipping function has to project the desired torque vector from the motion control algorithm into the safe region in the torque vector space. One solution is to nd the safe torque vector closest to the desired torque vector. Note that it is not always suf- cient to scale down the desired torque vector since the 0-torque vector is not always in the safe region. Amoving robot in Zero-G can increase its impactergy without any further torques applied to the system. In this case the Impact Controller will slow the robot down to enforce the safe region constraints. If the robot is pushed out of the safe region by external forces, the right side of Equation 8 becomes negative and the Impact-Controller enforces a reduction of the impactergy according to the time constant t c. 6.6 Implementing Impact Control To implement the proposed scheme which guarantees limited impactergy during autonomous motions of the robot the constraints have to be transfered into motor torque space. Considering that p (t) = p ((t); _ (t)), Equation 8 can be expanded to the following p (; ) _ p(; 1 ( max, p (; )) _ 8p 2 P (9) t c Inequality 9 represents a system of state dependent constraints on the accelerations _ of the joints. Using the dynamic model = M,1 ()(, N(; _ )) of the manipulator allows to expand (9) _ i hm,1 ()(, N(; ) _ p 1 ( max, p ) 8p 2 P (10) t c The Inequality 10 denes closed halfspaces in the torque space. The safe region in torque space is the intersection of the halfspaces. Note that the intersection of halfspaces is always convex.

9 6.7 Work in Progress The safety envelope formulation has been implemented and tested on a dynamic simulation of a planar 2DOF robot. The system is currently being implemented on a real robot The hardware platform for this project is a Barrett WAM robot, the commercial version of the MIT arm built by Townsend and Salisbury [13]. It has 7DOF and all joints are driven by cable transmissions. This allows very low friction, and thus, the robot can easily be mechanically backdriven by a person. 7 Conclusions In this paper, we presented a vision based humanmachine interface. The interface is able to in realtime reliably, accurately and robustly detect the position and orientation of the face. The system is nonintrusive and passive, thereby making it a natural interface. We also presented a new approach to humanfriendly robot control. We dened sensible goals for a human-friendly robot scheme, namely to ensure safety of all autonomous actions of the robot, while the user has the responsibility to use the robot safely. The basic Zero-G oating behaviour ensures that a user can understand and predict the robot's actions. The safetyof autonomous motion is guaranteed by the safety envelope. It guarantees that the maximum impact energy of the robot does not exceed a preset limit. References [1] A.Azarbayejani, T.Starner, B.Horowitz, and A.Pentland. Visually controlled graphics. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(6):602{605, [5] S.Bircheld and C.Tomasi. Elliptical Head Tracking Using Intensity Gradients and Color Histograms". In Proc. of Computer Vision and Pattern Recognition (CVPR'98), [6] A.Gee and R.Cipolla. Fast Visual Tracking by Temporal Consensus. Image and Vision Computing, 14(2):105{114, [7] Kentaro Toyama. Look, Ma { No Hands! Hands- Free Cursor Control with Real-time 3D Face Tracking. In Proc. of Workshop on Perceptual User Interface (PUI'98), [8] J.Heinzmann and A.Zelinsky. 3-D Facial Pose and Gaze Point Estimation using a Robust Real-Time Tracking Paradigm. In Proc. of the Int. Conf. on Automatic Face and Gesture Recognition, [9] R.Stiefelhagan, J.Yang, and A.Waibel. Tracking Eyes and Monitoring Eye Gaze. In Proc. of Workshop on Perceptual User Interface (PUI'97), [10] Y. Matsutmoto, T. Shibata, K. Sakai, M. Inaba, and H. Inoue. Real-time Color Stereo Vision System for a Mobile Robot based on Field Multiplexing. In Proc. of IEEE Int. Conf. on Robotics and Automation, pages 1934{1939, [11] John J. Craig. Introduction to Robotics. ln Addison Wesley, 2nd edition, [12] Ian D. Walker. Impact congurations and measures for kinematically redundant and multiple armed robot systems. IEEE Trans. Robotics and Automation, 10(5):670{683, [13] W.T.Townsend and J.K.Salisbury. Mechanical Design for Whole-Arm Manipulation. In Robots and Biological Systems: Toward a New Bionics?, pages 153{164, [2] A.Zelinsky and J.Heinzmann. Real-time Visual Recognition of Facial Gestures for Human Computer Interaction. In Proc. of the Int. Conf. on Automatic Face and Gesture Recognition, pages 351{ 356, [3] P.Ballard and G.C.Stockman. Controlling a Computer via Facial Aspect. IEEE Trans. Sys. Man and Cybernetics, 25(4):669{677, [4] Black and Yaccob. Tracking and Recognizing Rigid and Non-rigid Facial Motions Using Parametric Models of Image Motion. In Proc. of Int. Conf. on Computer Vision (ICCV'95), pages 374{381, 1995.