Student Projects

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

 

A Combined Approach
for Motion Learning and Obstacle Avoidance

Project: Semester (or Master) Project
Period: 15.02.2022 – 01.06.2022
Type: 20% theory, 40% simulation & software,
40% robot implementation
Knowledge: Programming skills: Python (MATLAB/C++); ROS; Machine learning; Control, Linear Algebra
Subject: Robotics, Software, Control, Machine Learning, Linear Algebra
Supervision: Huber Lukas ([email protected]),
Prof. Aude Billard

Introduction

Robots navigating in human-inhabited, unstructured environments have to plan or learn the initial path in advance, but they encounter disturbances constantly. In milliseconds a flexible, yet safe control scheme must take the right decisions to avoid collisions. Dynamical systems (DS) have proven to be an ideal framework in such dynamic environments with robot arms [1]. DS allow fast evaluation to perform collision avoidance in real-time [2], [3]. Moreover, they can be used for complex, but safe motion learning [4].
Motion learning and collision avoidance is often regarded as two independent problems, but they have many similarities. This project investigates the unification of motion learning and obstacle avoidance.

Goal of the Project

The goal of this project is to take existing algorithms for obstacle avoidance and motion learning and apply them to existing, as well as newly generated data, and finally to a collaborative robot arm.
• Use dynamical system based motion learning and obstacle avoidance frame work
• Adapt the obstacle environment algorithm for three dimensional environments
• Test and record motion with 6 degree of freedom robot arm
• Evaluate and document results

Expected Learning for the Student

• Gain deep understanding of dynamical systems based control and learning algorithms
• Testing in simulation and real life scenarios with a robot arm.
• Learning and applying research methods in robotics.

References

[1] K. Kronander and A. Billard, “Passive interaction control with dynamical systems,” IEEE Robotics and Au-
tomation Letters, vol. 1, no. 1, pp. 106–113, 2015.
[2] L. Huber, A. Billard, and J.-J. Slotine, “Avoidance of convex and concave obstacles with convergence ensured
through contraction,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1462–1469, 2019.
[3] L. Huber, J.-J. Slotine, and A. Billard, “Avoiding dense and dynamic obstacles in enclosed spaces: Application
to moving in a simulated crowd,” arXiv e-prints, arXiv–2105, 2021.
[4] S. M. Khansari-Zadeh and A. Billard, “Learning stable nonlinear dynamical

Learning Nonlinear Limit Cycles from Demonstration

Introduction

Limit cycles are frequently exploited in robotic applications where a periodic motion is desirable (e.g. polishing, walking, joint excitation, etc). A closed-loop trajectory of a dynamical system (DS) forms a stable limit cycle if all the neighbor trajectories converge to it. However, robustly controlling a robot in order to follow such a trajectory is not trivial, especially when it comes to nonlinear limit cycles. Learning the desired DS from demonstration can ensure stable tracking of complex trajectories. In this project, we are going to learn different DSs which contain a stable limit cycle with a nonlinear profile( see Figure 1).
 

Approach

From the Lyapunov stability criterion, we first propose a general form of potential function from which we can extract a dynamical system with stable limit cycles. Once the sample trajectories are recorded, we will use a nonparametric Bayesian approach (here GPR) to learn the target process of limit cycles from the demonstration.
 

Motivation

The project helps you: (a) understanding dynamical systems and the concept of learning from demonstration (b) developing and applying Gaussian Process Models in a practical example. (c) implementing the learned model on a robotic arm (KUKA IIWA 14).
 
ProjectSemester Project (LASA)
PeriodSept.21st, 2021 – Jan. 31st, 2021
Section(s)ME MT SV EL IN MA
Type50% theory, 50% software
Knowledge(s)Programming skills (Python / C++ / Matlab)
Subjects(s)Robot Planning; Control Theory;
Responsible(s)Farshad Khadivar
URLPaper; githup
AvailabilityAvailable

Incrementally Learning and Exploiting Inverse Dynamics

Introduction:

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation.
However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning perspective (e.g., input space dimension, amount of data needed). The accuracy of such models, regardless of the learning techniques, relies on proper excitation and exploration of the robot’s configuration space, in order to collect a rich dataset. This study aims to Incrementally explore the robot’s workspace to provide rich data in learning the inverse dynamics of a serial robotic manipulator using supervised machine learning techniques.
 

Approach

Our learning approach, in this project, is based on exploration and exploitation. We assume that a “rigid body dynamic” model of the robot, however inaccurate, is apriori known. Then we will use this model to extract and optimize the most informative set of trajectories online. While moving along the acquired trajectory, the recorded robot states will be exploited to update the dynamic model. Since the learning rate is much slower than the robot control frequency, this scenario ought to be executed incrementally.
 

Motivation

The project helps you: (a) understanding inverse dynamics model of a robotic manipulator (b) developing and applying Bayesian optimization framework in a real robotic application. (c) implementing the learned model on a robotic arm (KUKA IIWA 14)
ProjectSemester Project (LASA)
PeriodSept.21st, 2021 – Jan. 31st, 2021
Section(s)ME MT SV EL IN MA
Type50% theory, 50% software
Knowledge(s)Programming skills (Python / C++ / Matlab)
Control Systems; ROS (a plus); Rviz (a plus)
Subjects(s)Robot Dynamics; Control Theory; Bayesian Optimization
Responsible(s)Farshad Khadivar
URLgithub
AvailabilityAvailable

Master Projects at EPFL

Swarm Furniture Obstacle Avoidance for Smart-Living Environment Assisting Wheelchair Navigation

Description:

This project is a step towards the development of a modular control architecture for collaborative robots in smart-living environments. The goal is to develop a simulation environment of this fully smart home (mobile furniture) and implement a control framework for the modular robotic system to facilitate user’s navigation and obstacle avoidance through the smart home. The project will start by analyzing and drafting a swarm robot formation that could work in decentralised control based on LASA’s developed obstacle avoidance through modulated dynamical systems [1]. The resulting controller should plan motions either to move away or towards a person while respecting the environment’s constraints and avoiding collisions.

The test-bed simulation with modular robotic furniture will operate autonomously for increasing the efficient navigation of a pedestrian user or a smart wheelchair robot user (simulated semi-autonomous driving Qolo [2]). The resulting simulator and controller will serve as a baseline for enabling further development of the human-robot interaction framework to control specific behaviours of the modular robot furniture, such as a user commanding furniture to get closer or move further away.

This project is associated with the CIS Intelligent Assistive Robotics Collaboration Research Pillar. It is co-supervised by LASA and BioRob laboratories.

Approach

  1. Develop a test-bed environment (in Unity or Rviz) containing robotic furniture that incorporates physical/computational constraints and velocity control.

  2. Implement an obstacle avoidance DS-based algorithm on each robot object acting independently [1].

  3. Analyze the behaviour of the modular swarm acting independently and evaluate the performance of the swarm to provide a set of baseline results.

  4. Formulate a swarm controller for enhancing group response towards enhancing/facilitating user motion in the smart environment.

Expected Experiences for the Student

• Experience developing experimental test-bed for robot swarm simulation.

• Learning state-of-the-art motion control through time-invariant dynamical systems for mobile robots. 

• Experience virtual robot motion planning for a robotic swarm.

• Experience virtual robot dynamic control.

References:

  1. [1]  L. Huber, A. Billard, and J.-J. Slotine, “Avoidance of Convex and Concave Obstacles With Convergence Ensured Through Contraction,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1462–1469, 2019. DOI: 10.1109/LRA.2019.2893676

  2. [2]  D. F. Paez-Granados, H. Kadone, and K. Suzuki, “Unpowered Lower-Body Exoskeleton with Torso Lifting Mechanism for Supporting Sit-to-Stand Transitions,” in IEEE International Conference on Intelligent Robots and Systems, (Madrid), pp. 2755–2761, IEEE Xplorer, 2018. DOI:10.1109/IROS.2018.8594199

ProjectMaster Thesis (LASA/BioRob)
PeriodFebruary 2022 – July 2022
Section(s)ME MT SV EL IN MA
Type50% theory, 50% software
Knowledge(s)Programming skills (Python / C++ / Unity)
Control Systems; ROS (a plus); Rviz (a plus)
Subjects(s)Robot control; Control Theory; Simulation; Obstacle Avoidance
Responsible(s)Lukas Huber, Dr. Diego Paez, Dr. Anastasia Bolotnikova (BioRob)
URLCIS project, GitHub code
AvailabilityAvailable

 

Master Project in Industry

Data analysis of hand dexterity in microsurgery

Description:

Microsurgery demands superior dexterity in combination with excellent visuospatial skills, that can be acquired only through long training periods. To improve our understanding of these skills, we follow a cohort of microsurgeons during short microsurgery courses, conducted at the University Hospitals in Geneva (HUG).

In this master thesis, the student will participate in the design of the sensors to record hand position, force application and movement changes. The student will start from an existing set-up that uses miniature pressure sensors mounted on gloves and covering the instruments and infra-red optitrack motion tracking. The student will design a new arrangement of the sensors to avoid visual obstruction. Addition of other measurement units such as EMG and RGBD vision tracking will be considered. The student will actively participate in the statistical analysis of the data gathered to date and use this information to guide the placement of the sensors. The student will also participate in the modeling of the dynamics of hand motions and pressure, by developing graphical displays and by using machine learning techniques to model the temporal evolution of these signals.

The MSc thesis is part of a collaboration between the EPFL Learning Algorithms and Systems Laboratory (LASA EPFL) under the supervision of Prof. Aude Billard and at the University Hospitals in Geneva under the supervision of Prof. Torstein Meling. It is expected that the student spends significant time at the HUG and at the EPFL.

ProjectMsc in industry (LASA/HUG)
PeriodSpring 2022-Autumn 2022
Section(s)ME MT SV EL IN MA
Type40% theory; 40% software; 10% hardware
Knowledge(s)Programming skills (Python / C++ / Unity); Signal processing; ROS (a plus)
Subject(s)Machine Learning; Data Analysis; Signal processing; Simulation
Responsible(s)Prof Aude Billard and Dominic Reber
URL
AvailabilityAvailable

 

Imitation learning for delicate robotic motions in microsurgery

Introduction:

Microsurgery provides a challenging environment for robotic procedures due to difficult conditions under microscopic vision and delicate control of surgical tools.
Further parameters such as soft tissue interaction caused by various agents have to be considered as well.
Modeling such complex scenarios with classis tools is cumbersome and error-prone, often rendering an approach unsafe or impossible.
However, recent achievements in machine learning show that learning these fine-grained motions instead is within reach and could enable robotic procedures in microsurgery.
 
This thesis is a collaboration with Carl Zeiss AG
Turning today’s research into tomorrow’s applications – together
With a clear focus on user needs, the ZEISS Innovation Hub @ KIT explores new technology and application fields, enabling the transformation of ideas into innovations.
Here, students, researchers, entrepreneurs, and ZEISS employees meet at eye level and practice an open-innovation culture.

Approach
The student will work on an experimental setup consisting of a stereo-vision system for image guidance and a high precision industrial robot for tool manipulation.
He or she will setup an imitation learning system, using ROS or Matlab Simulink, that learns tool motions specific to a well-defined part of the surgery.
The thesis will include acquisition of training data, further examples including video data of expert surgeons could enrich the data set for learning.
The student will start with basic motions covering well defined tasks during the surgery, thereby either adapting the methodology or moving to more complex steps.

ProjectMaster Thesis (Industry) or Internship
Periodspring 2022 – Autumn 2022
Section(s)ME MT SV EL IN MA
Type30% theory; 60% software; 10% hardware
Knowledge(s)Programming skills (Python /C++/Matlab); ROS a plus
Subject(s)Machine Learning; Robot Control; Simulation
Responsible(s)Prof Aude Billard and Dominic Reber
URL Zeiss InnoHub KIT
AvailabilityAvailable

 

Master Project in Industry

Prototyping the flexible assembly production line using smart manipulators

Description

In industrial manufacturing large series of identical products can very efficiently be manufactured on automated production lines with a one-piece flow. On the other hand, when series are very low, assisted manual assembly is the most cost-effective solution even in high-wage countries. The most challenging scenario is however the mid-volume production ranges, where neither a dedicated production line is cost-effective and labor intensity is too costly to be executed by humans.

 

This gap can be filled by Industry 4.0 assisted flexible production lines equipped with smart manipulator robots.
In the project we will look into feasibility of prototyping such a solution where manipulators would require minimal time to switch production from one product group to other product group. In the frame of the project we will assess its feasibility and correctness of the obtained results and look for ways to improve it.

This project is in collaboration with Johnson Electric (JE) and is to be done in Murten and on Campus

Johnson Electric (JE) and EPFL are working together to develop a proof-of-concept tool which would solve the described problem.

ProjectMaster Thesis (Industry) or Internship
Periodspring 2022 – Autumn 2022
Section(s)ME MT SV EL IN MA
Type30% theory; 60% software; 10% hardware
Knowledge(s)Programming skills (Python /C++/Matlab); ROS a plus
Subject(s)Machine Learning; Robot Control; Simulation
Responsible(s)Prof Aude Billard
URL 
AvailabilityAvailable