ETH Zurich :
Computer Science :
Pervasive Computing :
Distributed Systems :
Smart Systems Seminar FS2018
Various topics from Ubiquitous Computing, Human Computer Interaction, Robotics and Digital Fabrication
Prof. Dr. Otmar Hilliges
Prof. Dr. Stelian Coros
Prof. Dr. Friedemann Mattern
The seminar is complete and will no longer accept any new registrations.
Time and Place
Tuesdays, 11:15 - 13:00, Room CAB G 52
The goal of the seminar is not only to familiarize students with exciting new research topics, but also to teach basic scientific writing and oral presentation skills.
The seminar consists of talks given by students on selected topics and discussions led by the instructors. A maximum of 12 students will be admitted to the seminar. Priority will be given to Master students who have sufficient background knowledge in the topic but the seminar is generally open to Bachelor and Doctoral students as well.
Seminar attendees select a specific topic within the broader context of current research and prepare an oral presentation. As a starting point, the students are assigned 3-4 important papers in their topic but they have to collect complementary materials and compile them together. Oral presentations must be planned for 45 minutes. Each presentation will be followed by a technical discussion as well as a short feedback session on the quality/style of the presentation. Each student also has to write a short essay on the selected topic. Essays must be composed using a given template and must be of length 5-8 pages (including figures, tables, and references). The essay is due in 3 weeks after the presentation. The quality of this essay will be evaluated and considered for the final grade.
It is not sufficient to present the selected papers only, the students should do independent literature research and in their presentations and essays they should summarize the whole topic.
Each student will have a tutor (typically a research assistant from the Advanced Interactive Technologies Group, the Distributed Systems Group, or the Computational Robotics Lab) assigned with whom they can discuss their papers in detail and receive preliminary feedback on their presentation and their essay.
The seminar will be held in English. Presentations and reports must be in English. Attendees are required to participate in all sessions.
Please use the following templates for your presentation and for your report.
The final grade is based on:
Students who successfully complete the seminar will be awarded 2 credit points (ECTS).
- the quality of the presentation;
- the quality of the essay;
- participation in discussions and feedback sessions after each presentation.
The list of topics is provided below.
1) Eye Gaze and Intelligent User Interfaces
Eye tracking is the process of measuring the point of gaze or where the user is looking. This area of research has a long history in medicine and psychology as a tool for understanding human behavior. However, most of the application domains have relied on eye tracking as a passive analysis tool. Recent technological advancements together with affordable eye trackers have created an interest to develop attention and gaze-aware systems. Our eyes can be used as an additional input modality to control user interfaces, help people with disabilities, or create more immersive video games.
Supervisor: Mihai Bâce
2) Sharing Experiences in Augmented and Virtual Reality Environments
Augmented Reality (AR) and Virtual Reality (VR) create exciting opportunities to engage people in immersive experiences. However, most such applications are designed based on the first-person point of view, of which one drawback is that the experience is user-specific. To solve this problem, immersive collaboration systems are designed to simultaneously involve more people to communicate using video, audio, and gestures. These systems not only tackle practical issues in industry, but also provide a new prototype of social media platform.
Supervisor: Jing Yang
3) Wearables in Healthcare and Wellbeing
Ubiquitous sensor technology and the proliferation of wearable devices to measure physiological data -- such as fitness trackers -- pose new opportunities to management and understand chronic conditions. Through remote and continuous monitoring we can get a better understanding of patients daily life, routine and vital signs. Moreover, the correct interpretation of these data allows space for the development of computational models that would help patients in the self management of their diseases. For example, warning them when their condition is about to worsen, enabling them to take the right measurements.
Supervisor: Liliana Barrios
4) Fast Visual Object Detection
The visual channel is one of the main cues used by humans. In order to create human-computer interaction systems and model the human’s intentions, it is essential to understand what the human sees. A crucial challenge is to detect and recognize the objects in the user’s field of view. Recently developed object detection algorithms based on convolutional neural networks make this possible.
Supervisor: Vincent Becker
5) Human Motion Prediction
Creating and understanding digital representations of human motion is a long-standing problem in Computer Science and a central aspect in many related research fields such as Computer Graphics, Computer Vision, Robotics, or Biomechanics. Human motion is inherently complex because it is non-linear, high-dimensional and subject to great variability. Furthermore, we as humans are exceptionally skilled at detecting flaws and irregularities in human motion, which sets a high bar for any computational method producing such motion. The suggested papers deal with extracting or generating a human skeleton in time series data using neural networks.
Supervisor: Manuel Kaufmann
6) Human Pose Estimation in the Wild
Estimating human poses is one of the core problems in computer vision and has many applications in life sciences, computer animation and the growing fields of robotics, augmented and virtual reality. Accurate pose estimates can also drastically improve the performance of activity recognition and high-level analysis of videos. Recent pose estimation methods have exploited deep convolutional networks (ConvNets) for body-part detection in single, fully unconstrained images.
Supervisor: Jie Song
7) Policy Search Methods
Policy search methods form a class of algorithms used for finding near optimal control policies for robots, or in general, for solving sequential decision making problems. The standard formulation of the sequential decision making problem assumes discrete action space. This problem can be solved using value function based methods like Q-learning or SARSA. In cases when the action space is continuous, previous methods are hard to apply. For problems with continuous action space, the policy search methods are preferred. In contrast to value function methods which focus on estimating the value function, policy search methods take different approach by searching the policy function directly.
Supervisor: Stefan Stevsic
8) Reinforcement Learning for Discrete Problems
Sequential decision problems are common in robotics and computer vision. Such problems can be tackled by an agent that receives input at each time step and selects an action to perform. A reward signal indicates whether an action was desirable and the goal is to maximize the average reward per step.
With accurate models a sequence of desirable actions can be selected with planning methods. However, when no good models are available or when the problem dimensionality is high such planning methods quickly become infeasible. Reinforcement learning deals with learning such agents from simulations when no direct supervision can be given. This topic will include the discussion of POMDP formalism, tabular methods for reinforcement learning (SARSA, Q-Learning, TD-Lambda, Eligibility Traces) and recent methods such as DQN where neural networks are used as function approximators.
Supervisor: Benjamin Hepp
9) Sensing in soft robots: methods, systems and implementations
Soft robots have infinite degrees of freedom, and by changing their shape, they can interact either passively or actively with their environment. The change in the shape of a soft robot can be used to infer the external forces generated through passive interactions with the environment or to reason about the active actuation forces required to control its motions. Direct ways of measuring the contact forces between the soft robots and its environment have also been developed. The scope of the current topic is to explore what types of sensing methods are utilized, what kinds of sensing systems have been developed and how they are implemented within the context of soft robotics.
- Tech Note: Digital Foam, 3D User Interfaces '08
- Batch Fabrication of Customizable Silicone-Textile Composite Capacitive Strain Sensors for Human Motion Tracking, Adv. Mater. Technol., vol. 2, no. 9, '17
- Macrobend optical sensing for pose measurement in soft robot arms, Smart Mater. Struct., vol. 24, no. 12, '15
- 3-Dimensional soft shape sensor based on dual-layer orthogonal fiber Bragg grating mesh, OFC '17
- Embedded electro-conductive yarn for shape sensing of soft robotic manipulators, EMBC '15
Supervisor: Nitish Kumar
10) Learning Approaches for Physically Based Environments
Recent technological advances in general purpose parallel computing put Deep Learning at the core of many fundamental problems in Computer Science. Physics-based animation and robotics are prime examples of areas where promising learning approaching have been proposed to learn physics models, to build fast physics simulation and/or fast decision making algorithms. The scope of this topic is to get familiar with different Machine Learning strategies used in the context of physics simulation and dynamic locomotion for virtual characters.
Supervisor: Vittorio Megaro
11) Soft Robot Modeling and Control
Soft robots naturally conform to their environment (be this the floor, or a target objects, etc.). This fact makes soft robots inherently safe, and has the promise to enable high performance in the face of environmental uncertainty. However, this fact also makes controlling soft robots very challenging. There is currently no one accepted answer for how to best control even simple soft robots, and state of the art fabricated examples often make use of custom, user-scripted input signals. The scope of this seminar topic is to learn about the different ways researchers have tried to model and control soft robots. Your focus should encompass both control methods that were made specifically for a single given robot, as well as general-purpose control methodologies that could apply to a broad class of soft robots.
Supervisor: James Bern
12) Dexterous Object Manipulation using Robots
Using manipulator robots for tasks like pick-and-place of rigid objects or assembling of different parts is nowadays state of the art in industry. In order to fulfill more complex tasks, it is becoming a requirement for robots to manipulate objects as dexterously as humans do. To have such capabilities, the robots need to possess a deep understanding of the physical world of deformable systems in order to plan and execute movements in a desired way.
Supervisor: Simon Zimmerman
For further information please contact Mihai Bâce.