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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

   
   

Adaptive human-robot interaction: From human intention to compliant robotic behavior

Robots are mainly here to assist us with tasks that are repetitive and burdensome. Machine learning and control theory provided us with a variety of techniques to teach our robots to perform such tasks. However, the ability of robots to adapt their tasks to their environment or to the intention of their human-user is limited. Providing robots with such adaptive abilities will unlock new possibilities for assistive robotics. Consider polishing as a task for a robotic arm. The robot learns how to polish from human demonstrations. However, during polishing, the human-user can safely grab the robot and change the polishing direction by applying few repetitions of movements in a new desirable direction. This means that the robots quickly adapts its motions to the intention of the human, thus, assisting him/her in performing the new task.

Previously, as the first step, we proposed a method for adapting the robot’s behavior to the intention of a human-user. This method is implemented/tested in simulation, and well-documented here. For the next step, the student will implement this method on a real robot. We will be using 7-DOF Kuka LWR 4+. An impedance controller will be provided to control the end-effector of the robot, and the student will mostly focus on adaptive motion planning using dynamical system. The method will be implemented in C++ using ROS libraries. At the end, we expect a compliant robot that polishes a surface and adapts its behavior (i.e., the location and the shape of the polishing) to the motions of the human.

Project: Semester Project
Period: 01.08.2017 - 01.02.2018
Section(s): IN MA ME MT MX
Type: 10% theory, 30% software, 60% implementation
Knowledge(s): Basic of Robotics and control, and C++ programming,
Subject(s): Physical Human-robot interaction, Adaptive control
Responsible(s): Mahdi Khoramshahi
   
   
   

Learning Manipulation with 4 Robotic Arms

Many industrial tasks require to have several robotic arms working on the same piece simultaneously. This is very difficuly as we want the robot to perform the task but do not intercept each other. The joint workspace of the robot is highly non-convex and cannot be expressed mathematically. This project will apply machine learning techniques to learn a representation of the feasible workspaces f 4 robotic arms. This representation will then be used in an inverse kinematic controller to control for the robot's motions at run time. The algorithm will be validated to control 4 robotic arm in the lab that must manipulate objects on a moving conveyer belt.

Project: Semester Project
Period: 01.01.2017 - 15.07.2018
Section(s): EL IN MA ME MT PH
Type:
Knowledge(s):
Subject(s): Robotics, Machine Learning
Responsible(s): Aude Billard
   

Master Projects at EPFL

   
   

Robust Bimanual Reaching motion for ABB-Yumi Robot

To perform many of our daily tasks, we use our both arms (and hands). This fact allows us to have a better control over our environment (better perception, higher precision, higher degrees of actuations, and higher applied forces). Given the uncertainties in our surrounding environment, our ability to coordinate the motion of our arms is extraordinary. Endowing robots with the same ability would increase their performance in the interaction with uncertain environments. Imagine a scenario where the robotic task is to grasp an object with imprecise location (only a probability distribution is available). This imprecision can be due to noisy perceptions or the fact that the object is moving with unknown dynamics. In such conditions, taking the maximum likelihood for granted and performing the task in a deterministic fashion might lead to poor performances and even failures. However, the robot can perform exploratory motions to gain better knowledge about the environment (i.e., a probability distribution with higher confidence for the target) which in turn would increase the performance of the task.

As the first step in this project, the student will focus on the formulation of a simple algorithm for simultaneous estimation and motion planning (using Kalman filters and dynamical system). As the second step, the student will implement and test this algorithm using ABB Yumi robot. The implementation will be done in C++ using ROS libraries where the robot is controlled in position. At the end, we expect a bimanual robot that grasps objects efficiently under environmental uncertainties.

Project: Master Project at EPFL
Period: 01.08.2017 - 01.02.2018
Section(s): EL IN MA ME MT MX
Type: 30% theory, 30% software, 40% implementation
Knowledge(s): Basics of Robotics and control, and C++ programming
Subject(s): Estimation, Motion planning, and Control
Responsible(s): Mahdi Khoramshahi
   
   
   

Learning Manipulation with 4 Robotic Arms

Many industrial tasks require to have several robotic arms working on the same piece simultaneously. This is difficult as the robot should not intercept each other while performing the task. The joint workspace of the robot is highly non-convex and cannot be expressed mathematically. This project will apply machine learning techniques to learn a representation of the feasible workspaces f 4 robotic arms. This representation will then be used in an inverse kinematic controller to control for the robot's motions at run time. The algorithm will be validated to control 4 robotic arm in the lab that must manipulate objects on a moving conveyer belt. It will also extend the approach to enable to manipulate the object under perurbations, such as when the conveyer belt slows down or accelerates rapidly.

Project: Master Project at EPFL
Period: 01.01.2017 - 15.07.2018
Section(s): EL IN MA ME MT PH
Type:
Knowledge(s):
Subject(s): Robotics, Machine Learning
Responsible(s): Aude Billard
   

Master Projects in Industry

   
   

Detection of product purchases from shelves in unconstrained, uncalibrated, heavily cluttered environments

Our work requires us to track changes within shelves in retail environments, corresponding to shoppers picking up products and buying them or putting them back on the shelves. Our methodology involves analysing video from one or more cameras recording up to 16h of store activity.
The task is complex as high numbers of shoppers move and occlude the videos when approaching the shelves, viewing angles are sometimes drifting throughout the day and there is no opportunity for calibration of the recording equipment when deploying in store.
The goal of the project is to leverage on the existing background subtraction system to integrate shopper occlusion and gradual camera movements to facilitate the detection process.
Knowledge requirements: Very good knowledge of C++ and principles of machine learning/computer vision (knowledge of OpenCV is a welcome plus).
Interested candidates should send an email to aude.billard@epfl.ch with a copy of their CV and grades.

Project: Master Project in Industry
Period: 01.09.2016 - 01.08.2017
Section(s): EL IN MT
Type:
Knowledge(s): Machine Learning, Programming in C++
Subject(s): Machine Learning
Responsible(s): , Basilio Norris
URL: Click here
   
   
   

Extraction of profiles of shopping and purchases patterns

In the scope of our shopper behaviour studies, we are confronted with the task of segmenting profiles of shoppers according to the type of purchases they make (categories of products, departments visited, …).
The goal of this project is to investigate the structure of shopping profiles in terms of clustering of sparse, high-dimensional data from hundreds of “shopping carts”, and to extract the main purchasing profiles and product category “proximities” according to the shopping behaviour rather than their relative location in store.
Knowledge requirements: Good understanding of Machine Learning concepts and tools (Clustering, Dimensional Reduction / Scaling, …). Good knowledge of at least one Machine Learning-related programming language (e.g. Matlab, R, Python, C++).

Interested candidates should send an email to aude.billard@epfl.ch with a copy of their CV and grades.

Project: Master Project in Industry
Period: 01.09.2016 - 01.08.2017
Section(s): EL IN MT
Type:
Knowledge(s): Machine Learning, Programming in C++ / Python / Matlab / R
Subject(s): Machine Learning
Responsible(s): , Basilio Norris
URL: Click here
   



Last update: 01/03/2012