STUK2007 articles: visits

Team D           Synergy
This was our second day in Edinburgh. Yesterday, we walked around t...
STUK2007 articles: visits

Cricket Landmarking Model
We were split into groups and watched the research work. The first...
STUK2007 articles: visits

kinematics chain problem. For example, the front leg directs the body of the stick insect to ...
STUK2007 articles: visits

two main areas such as pre-planning fighting scenes with multiple characters for movies and
STUK2007 articles: visits


Prof. Fisher giving his lecture to all of ustelling us his research projects

STUK2007 articles: visits

Prof. Fisher explaining the Model Reconstruction to us

Possible Options:
STUK2007 articles: visits

Machine learning can help to improve the movement of robots so that their movement can be
STUK2007 articles: visits


Q: What is the reason for the lab performing experiments on insects instead of other ki...
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U_Edinburgh.doc U_Edinburgh.doc 88KB Jul 26 2007 10:22:32 PM

  1. 1. STUK2007 articles: visits Team D Synergy This was our second day in Edinburgh. Yesterday, we walked around the city and went to some scenic spots. Early this morning, we rode on a bus to reach our destination - the King’s Building of the University Edinburgh. Selected: (curved in PS, find the adjusted one in InDesign Source) Possible Optional: The Cricket Lab When we arrived at the building, it was similar to buildings around the city - antiquated. We first visited DrDr.. Barbara Webb’s Cricket Lab. She briefly introduced her lab to us. The lab is conducting research that aims to understand the behaviour of insects, develop a computational model of such behaviour, and mimic the behaviour in a robot. The behaviour of insects are being replicated by robots that are built from electronic circuits and chips and these robots will then be put through the same experiments as the insects in order to verify or fine-tune the understanding of their behaviour.
  2. 2. STUK2007 articles: visits Cricket Landmarking Model We were split into groups and watched the research work. The first one was the Cricket Landmarking Model. DrDr.. Jan Wessnitzer, the research associate of DrDr.. Webb, explained to us the detail of this project. In the experiment, a cricket is placed in a very hot container with several cool spots. It is to find out the learning behaviour of crickets. There were also landmarks on the wall of the container aiding the crickets to recognize the locations of the cool spots. Selected: Caption: DrDr.. Jan Wessnitzer explaining the Landmarking experiment to all of us Possible Options: Initially, the crickets randomly explored until they found the cool spot. It has been found that the behaviour of crickets will be different and they take less time to locate the cool spots after several trials. The relation among the landmarks and the behaviour is being studied. Crickets have probably learned something so that they can find the cool spots quickly. The brain of an insect is a distinct structure. The mushroom body is expected to be associated with memory, as well as learning. The neuroarchitecture in the mushroom body is suited for pattern recognition. A computational model is then constructed to replicate the behaviour as observed from the crickets. According to Jan, the lives of crickets will be shortened after the experiment. Ant Navigation Model Next, Michael Mangan, one of the Ph.D. students, told us about the Ant Navigation Model. The behaviour of a certain type of ant was being studied. The species being used was one living in a hot environment such as a desert. Experiments have also been done on their navigation ability in barren and crowded environments. Ants are able to navigate complicated paths and find their way back home. The sun acts as a compass while the ants are looking for food and in a desert environment, the ants use visual recognition rather than chemical recognition during navigation. This is because chemicals disappear quickly in a hot environment. An un-intuitive finding of this experiment is that each ant individually does navigation on its own and it is not a collective behaviour. Robots are being built in their lab to mimic this unique behaviour. Stick-insect Walking Model Another research student, Hugo Rosano, presented us a Stick-insect Walking Model. He found that the legs of stick insects are independent and communication between the legs is limited. The neuro-system organizes the walking control. As each of the legs has 3 joints, if one of the joints moves, altogether 18 joints have to move in a manner which is congruent to the movement. When the joints experience a force, they follow the movement and form an organized movement of the insect. The body motion relates to the legs. Such motion mechanism can be modelled based on positive feedback controllers and explains the close
  3. 3. STUK2007 articles: visits kinematics chain problem. For example, the front leg directs the body of the stick insect to the target. All the legs move in the same direction but in fact only the front leg leads the way. The mesothorax follows a specific line. The insect motion based on the model can be simulated by software. Lastly, Dr. Webb introduced some other research on crickets and fruit flies. Other Research on Crickets Using the crickets, experiments are done in a "box" to observe how they behave in an environment with different sounds, different wavelengths of sound, and even with different visual environments. Then, a robot model is built to mimic the behaviour of the crickets. The robot uses a logic circuit as the brain, a camera pointing at a mirror as the eye to provide a 360-degree view, and some wheels as legs. The robot is then put in the same environment as the crickets to observe its behaviour. The parameters will be changed in the experiments; for example, the period of the sound or the pitch of the sound. It is found that crickets will perform different types of behaviour since each species has their own unique melodies and they can recognize other species. Crickets are chosen for the research because the sound localization in crickets is already well understood and "there is still a lot to learn from even simple animals" as stated by DrDr.. Webb. Selected: Caption: DrDr.. Webb showing Sharon the cricket used in the experiment OR Dr. Webb showing us the cricket used in the experiment Possible Options: Other Research on Flies Using fruit flies, in an ongoing Ph.D.PhD work, the behaviour of flies under visual and chemical stimuli are being studied. The flight of fruit flies is tracked three dimensionally by two cameras. The visual surroundings are changed to study how fruit flies change their behaviour. For example, they need to avoid walls when flying around. The visual stimulus causes them to make a quick turn and move away from the walls. The study is then further combined with chemical stimuli such as those when they are looking for fruit. It is interesting to make different sensory systems interact with each other. Indeed, fruit flies are being modelled by a robot arm which can move a camera around at different speeds and performs in the same way as flies. Motion Capture for Game Simulation After we visited DrDr.. Barbara Webb’s “Cricket Lab”, we followed DrDr.. Taku Komura to a lecture room where he gave his presentation. Although the building is antiquated, the facilities in the lecture room are modern. The Fight Simulation There were two projects that DrDr.. Komura presented to us. The first one was about the generation of fight animation. The objective of this project is to generate realistic fighting scenes for more than one avatar and emulate different styles of fighting. It can be applied to
  4. 4. STUK2007 articles: visits two main areas such as pre-planning fighting scenes with multiple characters for movies and generating fighting animation for video games. DrDr.. Komura explained the idea of the project. There are some pre-processing steps. First, different fight motions are captured with mocap, the optical motion capturer, to form a fight motion table. Second, a game tree of fight motions between avatars is built for fight simulation using temporal expansion. With the temporal expansion approach, the continuous nature of a fight is converted into a discrete strategy planning problem. Hence, motion of fight can be selected by fighters as options. When the simulation kicks off, fighters pick their motion option from the game tree. Selected: Caption: DrDr.. Komura showing us a demonstration clip of a Fight Simulation Possible Options: Fighters can have their own personalities such as being smart, less-intelligent, energetic, tired, defensive, aggressive, and even out-fighter and in-fighter. These personalities affect the decision of picking the motion. For example, a smart fighter can go further down the tree to figure out which motion is the most beneficial to him. On the other hand, human players can assign high-level instructions to ask an avatar to perform particular motions. All in all, pre- planning fighting scenes has become an easy task. The Wrestling Simulation The later presentation introduced an ongoing project which deals with tangling simulation among virtual characters. The objective of this project is to develop a topological representation between the body segments of virtual characters during close interactions such as dancing and wrestling. It has some applicable areas such as motion synthesis, path-planning and contents-based retrieval for motions of more than two avatars and also, is useful for motion editing, re-targeting and real- time animations. This time, the project models wrestle and the idea of the project follows. First of all, wrestling motions are captured individually. Then, template postures are extracted from the captured data. By having the template postures, a topological relationship for wrestling among templates can be formed. After that, body segments on the templates are tangled together under human instruction. The tangling relationship is further derived from the tangled templates. Finally, elastic constraints are applied to the tangled body segment so that body segments do not untangle, and physical-based animations of wrestling are generated. In fact, the reason for wrestling motions being captured individually is that when capturing two persons at the same time, it is difficult to figure out the entire body segments by cameras due to possible occlusion. Selected: (Please adjust more bright)
  5. 5. STUK2007 articles: visits Caption: Prof. Fisher giving his lecture to all of ustelling us his research projects Possible Options: When DrDr.. Taku Komura was finishing his presentation, Prof. Robert Fisher came into the room. Prof. Fisher, a tall gentleman, was going to give us a "lecture" on 3D model reconstruction and video analysis. He told us that he came to Hong Kong for a conference last year and he was pretty familiar with downtown Hong Kong. Hence, he started to give us a scenario to brainstorm a problem - how can we reconstruct a 3D model of Hong Kong Bank by using images alone? This was the main goal of his first lecture - Virtual Model Completion. Virtual Model Completion When constructing a 3D virtual environment of a particular building, some pictures of the building are captured for constructing the 3D model. However, it is not feasible to acquire every picture of all angles of the building. This research is aimed to reconstruct an approximate 3D model from 2.5D images. The idea comes from research which extends 2D texture by filling in holes or areas with pixels. The algorithm copies pixels from the original texture and placing those pixels onto unfilled areas. Those pixels are selected according to the similarity of neighbouring pixels at the boundary. In 3D space, according to the lecture being taught by Prof. Fisher, the 2.5D image is first fitted onto the surface of a selected 3D shape. Then, the fitted surface forms a bump map and the map is further triangulated to find corresponding vertices. After that, by following the idea of extending 2D texture as mentioned above, the unfitted part of the shape can be reconstructed vertex by vertex. Those vertices are selected from the triangulated surface based on their similarity to the neighbouring boundary vertices. The distance from the targeting boundary vertex is also accounted for in the computation. Finally, by repeating the copying of vertices, an approximate 3D model is reconstructed from a 2.5D image. Selected: 7/in_tour/album/Day4/Sharon/DSCN1975.JPG Caption: Prof. Fisher explaining the Pisa Tower Model Reconstruction to usTyler making friend with Prof. Fisher OR
  6. 6. STUK2007 articles: visits Prof. Fisher explaining the Model Reconstruction to us Possible Options: Crowd Analysis After we enjoyed the lecture on virtual environment reconstruction, we were treated to another excellent lecture given by Prof. Bob Fisher. This lecture was about analyzing video to detect rare circumstances. This research was presented at the International Conference of Pattern Recognition held in Hong Kong last year. We were grateful to have a "private" presentation from Prof. Fisher. The goal of the research is to build a system to find rare circumstances, like accidents or fights in a crowd of people, from a pool of video tapes captured by surveillance cameras. Optical flow and feature dimensionality reduction are used in the analysis model. The analysis observes changes in clusters of pixels within a period of time so that rare changes can be detected. Pixels in video frames contain millions of pieces of data. Therefore, some pre-processing of the video data is carried out in order to extract the relevant data for analysis. In the pre- processing stage, actual motion flow in video frames is extracted from the background. The extracted flow is then transformed into optical flow vectors which represent the flow of pixels in a vector space. After the pre-processing stage, video frames are segmented and then clustered. Clusters of vectors are the ultimate target of being observed in each segment. Principal Components Analysis is then applied onto those clusters to analyze changes in different component dimensions of that particular segment. When the above stages are completed, video with the normal situation of a crowd is first analyzed. Then, video with an abnormal situation of a crowd is processed. It is shown that there is a distinct difference between the analysis result of the normal situation and abnormal situation. After the lecture, we went to have lunch in the canteen of the King's Building. Machine Learning When we finished our lunch in the canteen, we went back to the lecture room for the next presentation. Prof.DrDr. Sethu Vijayakumar, who is the director of the Institute of Perception, Action and Behaviour, was already there waiting for us to give his presentation. The presentation was related to one of his research areas - Statistical Machine Learning. And in his presentation, he showed us how the technology can be applied to robots and how robots benefit from it. Selected: Caption: We enjoying a speech from Prof. Sethu Vijayakumar Possible Options: Statistical Machine Learning
  7. 7. STUK2007 articles: visits Machine learning can help to improve the movement of robots so that their movement can be less stiff and have higher accuracy and faster speed. Machine learning can also help in the study of statistics from previous movements and make predictions as to what should come next and thus improve the next movement so that it can move smartly. Reactions to various inputs can be refined or improved by changing the “Internal model” which is actually a FeedForward Control implemented in the Control Policy. For human beings, we learn how to control our arms by practising but for robots, they have to learn from statistics and “Internal Models” can help a lot in this area. Dynamic Systems-based Movement Policies Also, Prof.DrDr. Vijayakumar talked about the Dynamic Systems-based Movement Policies which is about how to select which path or which motion to reach a certain point. One method is using trajectory following and Generalization. This method will capture the motion of a human being and will ask the robot to follow the motion. In this way, the robot is taught by the human. In machine learning, there are two types of system: open loop and closed loop systems. Open Loop means there are no sensors in the system. The motions are prerecorded and repeated. The robot will always give the same response no matter what the stimuli are. In a Closed Loop system, the system is equipped with sensors. The sensors will read the data and save them for statistical analysis. And the response by the robot will be based on the analysis results. For some operations, both open loop and closed loop systems are used. Visual Attention & Oculomotor Learning The next topic in Prof.DrDr. Vijayakumar’s presentation was about Visual Attention & Oculomotor Learning. Different from the previous talks, this topic is more focused on the sensory aspect instead of movement. In Prof.DrDr. Vijayakumar’s presentation, he showed us the MAVERic Project which is a versatile robotic vision head developed for oculo-motor research. The robot head can perform like a human. It pays attention to moving objects and can be distracted by other moving objects. We watched several videos on the experiment of the robot head capture from its camera. The response is really fast and it acts like a human. Final NoteDrive to Success Although the hosts are carrying out research in different areas, they all share the same drive in doing the research work. The drive is important in giving them energy to tackle problems and generate ideas. What is it? The drive is - the passion for doing Computer Science. Selected: %20Photos/IMGP1050.jpg Caption: A group photo with all of us in the lecture room Possible Options:
  8. 8. STUK2007 articles: visits Q&A: Q: What is the reason for the lab performing experiments on insects instead of other kinds of creatures? A: Insects are small but already complex. It is a good step before we investigate other kinds of creatures that are more complex. Q: How do algorithms handle exceptional cases like a bird flying over a crowd which covers some of the video frames or a car driving into the crowd and stopping? A: These cases affect the background model of the optical flow, but a dynamic learning model can be added to the algorithm in order to adjust the background model or just ignore those minor abnormalities as the bird would just appear in a few video frames. Q: Is there any application that works with the statistical learning model? A: Not yet. The research work mainly shows that we have the capabilities to do learning in very high dimensional complicated systems. The underlying science is going to work in building an autonomous system that learns purely from data. Team Sharing: “There is still a lot to learn from even simple animals.” This sentence was said by DrDr.. Webb. Indeed, this sentence is the reason why the Cricket Lab is carrying out research on insects and mimicking the behaviour of the insects. Even though an insect is small, it is already complex. Before mimicking the behaviour of complex creatures like human beings, it is an important step to mimic something smaller. That’s a good idea we need to bear in mind. Go step by step!