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Student Projects propositions

If you are interested in one of the below projects, Semester Projects, Master Projects at EPFL, or Master Projects in Industry please contact the first person of reference indicated in each description either by telephone, or by email, or by visiting us directly to the LASA offices.


Semester Projects

   
   

Learning assembly sequences from demonstrations

Coding analytically sequences of assembly tasks can be challenging and time consuming. In small assembly lines, uncertainty and flexibility are expected. Reprogramming a robot as a new task arises is, therefore, not an option. An alternative way to cope with this challenge is to learn the correct sequence of actions from demonstrations. The question becomes then, how much prior knowledge does the robot really requires? The aim of the project is to learn the correct sequence of actions that would lead to a successful assembly with the least prior knowledge possible. For example, given a set of objects on a table, and user demonstrations, the robot should infer what are the relations between objects and the meaning of common predicates such as on, in, under, below... Observing how those object relations evolves during the assembly process would then lead to the creation of a correct assembly plan. A final demonstration of the system on one of the robot of the laboratory is expected at the end of the project.

Project: Semester Project
Period: 01.01.2019 - 01.08.2019
Section(s): EL IN MA ME MT PH
Type: 50% theory, 50% implementation
Knowledge(s): Python/C++
Subject(s): Machine Learning
Responsible(s): Baptiste Busch, Athanasios Polydoros
 
   
   
   

Learning dynamic systems of force interaction tasks

Force interaction tasks are very common in the field of robotics. In such cases, the force feedback is crucial for the systemís accuracy, adaptability and robustness. Thus it is a factor which has to be taken into account when creating dynamic systemsí representations of tasks. Nevertheless, creating an analytical model of the dependencies between the robotís position and exerted force can be difficult or even impossible. Therefore, the goal of the project will be to employ machine learning methods which will create force-dependent dynamic systems. The learned systems could be able to model various tasks and adapt to perturbations. The use-case will be an assembly task and demonstrations of it will be performed by a human through kinesthetic teaching. During the demonstrations, force and position data will be recorded and used as a training set for the dynamic system.

Project: Semester Project
Period: 01.01.2019 - 01.08.2019
Section(s): EL IN MA ME MT PH
Type: 50% theory, 30% implementation, 20% software
Knowledge(s): Python/C++
Subject(s): Machine Learning, Robot Control
Responsible(s): Athanasios Polydoros
 
   
   
   

Inferring assembly objects from demonstrations

Programming robots by demonstration has been a popular approach because it enables non robotic experts to easily introduce a task to a robotic system and also significantly decreases the time needed for re-programming. Those characteristics are highly desirable for robotic manufacturing tasks such as assembly. Thus, the goal of the project will be to create an unsupervised learning system which will be capable to infer the needed objects to perform an assembly task. The information provided to the system will be a feed of images, captured from RGB-D sensors, during which a human will demonstrate the task. Also tactile sensing systems can be used.

Project: Semester Project
Period: 01.01.2019 - 01.08.2019
Section(s): EL IN MA ME MT PH
Type: 30% theory, 50% implementation, 20% software
Knowledge(s): Python/C++
Subject(s): Machine Learning, Robot Vision
Responsible(s): Athanasios Polydoros
 
   
   
   

Human-robot impedance matching as reaction strategy toward unexpected external forces

Any seamless physical human-robot interaction requires an efficient reaction strategy toward external forces. Consider a robot and human carrying a heavy object together. During this transportation, the robot senses a sudden external force. Regarding the reaction strategy toward this external force, the robot is facing a dilemma: either, the force is a disturbance and needs to be rejected by exhibiting an stiff behavior, or, the force is intentional and requires a compliant behavior. Generally, this is a challenging problem. However, the robot can benefit from the presence of the human in the interaction. Assuming that the human would exhibit the proper reaction (to be either stiff or compliant), the robot can mimic the humanís behavior; technically speaking, to match its impedance.

In this project, we will be focusing on impedance/admittance control structure and simple scenarios; for example, a human and a robot holding/stabilizing an large object jointly while an external force occurs. As the first step, the student needs to work on the theoretical aspect and proper formulation of the problem. The student can use control theory or machine learning approaches to offer a solution/algorithm to vary the robot stiffness. Based on the progress, the student can decide to work further on theoretical aspects of the problem (such as stability of the interaction under the variation of the stiffness), or on implementation or validation on a real robotic arm.

Project: Semester Project
Period: 01.01.2019 - 01.08.2019
Section(s): EL IN MA ME MT PH
Type: 40% theory, 20% software, 40% implementation
Knowledge(s): Robotics, Control, Matlab
Subject(s): Physical human-robot interaction
Responsible(s): Mahdi Khoramshahi
   
   
   

Learning the unmodeled dynamics of the task for implicit force control

Performing a contact tasks in an uncertain environment is a challenging robotic problem. Uncertainties in the location of the surface, normal vector of the surface, measurement noises, imperfection in the actuators, and other unmodeled dynamics introduce an error in the desired force applied by the robot. Fortunately, such many sources of such uncertainties are structural and can be modeled and correct for: the robot can learn (or adapt to) such unmodeled dynamics in its environment through interaction.

Previously, we developed a controller at LASA that can approach a surface and exert a desired force. A demonstration can be viewed here. Through this project (i.e., learning the unmodeled dynamics of the environment), we aim to improve the precision of the force profile. In this project, we will be using adaptive control approaches. This project will be mostly programming (C++/ROS) and implementation (on Kuka LWR robotic arm).

Project: Semester Project
Period: 01.01.2019 - 01.08.2019
Section(s): EL IN MA ME MT PH
Type: 20% theory, 40% software, 40% implementation
Knowledge(s): Robotics, Control, Matlab, C++
Subject(s): Physical human-robot interaction
Responsible(s): Mahdi Khoramshahi, Walid Amanhoud
   
   
   

Classification of human vs hard contacts through force sensing

Many robotic tasks involve contacts with surfaces. For example, consider polishing a surface or drilling an object. The most common approach to solve such problem is to rely on force-sensing: while approaching the surface, the contact is recognized when the sensed forces are passed beyond a threshold. Therefore, the robot begins to exert the desired forces. However, considering cluttered and uncertain environments including humans introduces new challenges for safety of such robotic task: for example, the robot might detect collide with a human and detects him/her as the expected hard contact. Therefore, following the same strategy, the robots harm the human applying its desired forces. An effective strategy would be to distinguish between hard contact surfaces and accidental contacts with humans.

Previously, we developed an algorithm at LASA to distinguish between accidental and international forces. A demonstration for this algorithm can be viewed here . The goal of this project is extend this approach to include more classes of interaction forces, such intentional/unintentional hard/soft contacts. In the first step of this project, the student will be focusing on collecting and investigation force-profiles resulting from such different scenarios. The aim is to identify and extract effective feature which allows us to design a classifier. If time and progress permit, the student can work on reaction strategy and robotic implementation.

Project: Semester Project
Period: 01.01.2019 - 01.08.2019
Section(s): EL IN MA ME MT PH
Type: 50% theory, 30% software, 20% implementation
Knowledge(s): Robotics, Control, Matlab
Subject(s): Physical human-robot interaction
Responsible(s): Mahdi Khoramshahi
   

Master Projects at EPFL

no projects


Master Projects in Industry

no projects




Last update: 01/03/2012