Open positions
Master thesis
Application deadline: 26/01/2024
PIC4SeR Thesis
Robot UWB Trajectory Evaluation with precise optical tracking system
Supervisor: Prof. Marcello Chiaberge, Alessandro Navone, Umberto Albertin, Gianluca Dara
Skills: Python Basic Level (Mandatory) – ROS2 (Recommended)
Code: 2024PIC01
Topic: Localization
Description: The goal of the thesis is to study and precisely evaluate robot localization algorithms based on Ultrawide-band sensors, collecting trajectory data with an advanced optical tracking system available at the PIC4SeR lab. The objective of the study is the collection of significant and reliable data to identify and characterize the UWB signal loss in Line-of-Sight (LOS) and NLOS conditions.
Robot Social Trajectory Evaluation with precise optical tracking system
Supervisor: Prof. Marcello Chiaberge, Andrea Ostuni, Gianluca Dara, Andrea Eirale
Skills: Python Basic Level (Mandatory) – ROS2 (Recommended)
Code: 2024PIC02
Topic: Social Navigation
Description: The goal of the thesis is to study and precisely evaluate social navigation algorithms collecting trajectory data with an advanced optical tracking system available at the PIC4SeR lab. The candidate will study the algorithms at hand and test them computing relevant metrics of the task. Social navigation systems need a combined tracking of robot and people motion to be properly evaluated.
Learning Heuristics for Adaptive Path Planning in Social Scenarios
Supervisor: Prof. Marcello Chiaberge, Andrea Ostuni, Mauro Martini, Andrea Eirale
Skills: Python, ROS2 (Mandatory) – Path Planning Theory, Deep Learning (Recommended)
Code: 2024PIC03
Topic: Social Navigation
Description: The goal of the thesis is to develop social version of classic path planning algorithms (A*) by learning additional behaviours required to smoothly navigate in social contexts.
Efficient People 4D pose estimation and tracking for social controllers
Supervisor: Prof. Marcello Chiaberge, Chiara Boretti, Andrea Ostuni, Martini, Andrea Eirale
Skills: Python (Mandatory) – Deep Learning Basic Level, Pytorch/Tensorflow (Recommended)
Code: 2024PIC04
Topic: Perception Computer Vision
Description: The goal of the thesis is to study, apply and evaluate state of the art solutions for visual people detection and tracking. The thesis aims at setting up a precise performance benchmark to compare people detection and tracking solution with real-time performance to be executed on the onboard hardware of a mobile robot. A second step will aim at identifying or developing an efficient model for detection and tracking of people moving. The model should provide at least a 4D pose of the person (3D bounding box including yaw orientation) to feed and enable social navigation controllers.
Sensor Fusion for SLAM using FUSE in challenging environments
Supervisor: Prof. Marcello Chiaberge, Giacomo Franchini, Andrea Ostuni, Mauro Martini
Skills: ROS 2, Robot Localization (Mandatory) – C++ (Recommended)
Code: 2024PIC05
Topic: Localization
Description: The goal of the thesis is to study, apply and evaluate state of the art solutions for visual people detection and tracking. The thesis aims at setting up a precise performance benchmark to compare people detection and tracking solution with real-time performance to be executed on the onboard hardware of a mobile robot. A second step will aim at identifying or developing an efficient model for detection and tracking of people moving. The model should provide at least a 4D pose of the person (3D bounding box including yaw orientation) to feed and enable social navigation controllers.
Reference e-mail: giacomo.franchini@polito.it
Semantic Obstacle Avoidance of Robotic Arm for Fruits Harvesting
Supervisor: Prof. Marcello Chiaberge, Marco Ambrosio, Alessandro Navone, Luigi Mazzara
Skills: Python (Mandatory) – Deep Learning Basic Level, Pytorch/Tensorflow, ROS 2 (Recommended)
Code: 2024PIC06
Topic: Robotic Arm Control
Description: The goal of the thesis is to study and develop an obstacle avoidance algorithm for robotic fruit grasping. The work aims at integrating a semantic knowledge of the scene received from a semantic segmentation model with the control system of the robot. The candidate will develop a smart system to match semantic information obtained from visual data (fruits, branches, vegetation) to point cloud commonly used for obstacle detection to compute collision-free trajectory to grasp the target object.
Multi-robot localization: belief propagation on factor graph
Supervisor: Prof. Marcello Chiaberge, Alessandro Navone, Mauro Martini, Marco Ambrosio, Giacomo Franchini
Skills: ROS 2, Robot Localization Theory, Object-Oriented Programming (Mandatory) – Probabilistic robotics background (Recommended)
Code: 2024PIC07
Topic: Localization
Description: Study and implement a Gaussian Belief Propagation method on a distributed multi-robot systems without the need of a centralized network node. The candidate will study the performance of such system on a set of multiple robots (from 3 to 15)
Optimization of large vision models for edge execution
Supervisor: Prof. Marcello Chiaberge, Simone Angarano, Mauro Martini, Chiara Boretti
Skills: Python (Mandatory) – Deep Learning Basic Level, Pytorch/Tensorflow (Recommended)
Code: 2024PIC08
Topic: Perception Computer Vision
Description: The goal of the thesis is to study, evaluate and optimize state of the art large vision-language models for robotic perception. A wide range of robotic applications need an accurate but efficient perception system. Combining most recent foundation models with optimization techniques (model scaling, pruning, quantization, transfer learning) may lead to overall improved prediction performances
Reference e-mail: simone.angarano@polito.it
Robust Autonomous Navigation in Vineyard for complete path coverage
Supervisor: Prof. Chiaberge, Marco Ambrosio, Alessandro Navone, Andrea Ostuni
Skills: Python/C++, ROS2 (Mandatory) – Hands-on experience with hardware and robotic platforms (Recommended)
Code: 2024PIC09
Topic: Navigation Optimization
Description: The objective of the thesis is to integrate, improve and complete algorithms for robust full-field navigation in vineyards (straight, curved, plain, slope). The candidate will study both localization-based and position-agnostic solutions for vineyards and orchards traversal, identify critical points to improve in the starting version of the project. Testinng will be carried out in both simulation and real world the resulting complete pipeline (inter-row navigation and intra-row navigation). The output of the work will be a behaviour system to orchestrate the complete navigation task and the evaluated performance.
Reference e-mail: marco.ambrosio@polito.it
Spectral imaging for fruit health monitoring
Supervisor: Prof. Marcello Chiaberge, Alessandro Navone, Luigi Mazzara, Francesco Messina, Gianluca Dara
Skills: Python (Mandatory) – Experience with spectral imaging sensors, Agronomy background (Recommended)
Code: 2024PIC10
Topic: Perception Computer Vision
Description: The goal of the thesis is to collect a structured dataset on field with a multi-spectral sensor and develop an efficient Deep Learning model to extract relevant feature to predict the desired fruit information. The thesis starts from a detailed study of agronomic patterns to detect in multi-spectral imaging and the realization of a dataset.
Reference e-mail: alessandro.navone@polito.it
Fault-tolerant DNN for Space Applications
Supervisor: Prof. Marcello Chiaberge, Simone Angarano, Mauro Martini, Umberto Albertin
Skills: Deep Learning Basic Level, Python (Mandatory) – Compression Techniques, Embedded Systems (Recommended)
Code: 2024PIC11
Topic: D.L. Space
Description: The thesis aims to develop methodologies for verifying the integrity of neural network parameters used for image analysis in the Edge-AI system. The idea is to avoid designing inherently robust neural networks for two reasons: 1) the robustness obtainable architecturally with appropriate training is never perfect or guaranteed, and 2) the networks thus developed pay for the acquired robustness with degradation in image analysis performance (e.g., the accuracy of a classifier). This WP will analyze approaches based on using a neural network not specifically designed to be robust, coupled with methodologies for the timely detection of parameter errors and their correction.
Reference e-mail: simone.angarano@polito.it
Knowledge Distillation for Domain Generalization
Supervisor: Prof. Marcello Chiaberge, Simone Angarano
Skills: Python, Deep Learning Advanced Level (Mandatory) – Pytorch/Tensorflow (Recommended)
Code: 2024PIC12
Topic: Perception Computer Vision
Description: Study and explore knowledge distillation techniques for multi-class segmentation, pose detection, object detection and relevant computer vision tasks for robotic perception.
Reference e-mail: simone.angarano@polito.it
Solving the Primacy Bias of Deep Reinforcement Learning with Dimensional Growing Neural Networks
Supervisor: Prof. Marcello Chiaberge, Andrea Eirale
Skills: Python, Deep Learning Advanced Level (Mandatory) – Machine Learning/Deep Learning Basic Level, Reinforcement Learning Basic Level (Recommended)
Code: 2024PIC13
Topic: R.L.
Industrial Facilities Monitoring using autonomous UAV
Supervisor: Prof. Marcello Chiaberge, Francesco Messina, Umberto Albertin
Skills: Python (Mandatory) – Matlab, Deep Learning Basic Level (Recommended)
Code: 2024PIC14
Topic: Computer Vision
Description: The goal of the thesis is to develop a Semantic Segmentation and Change Detection framework for multitemporal damage identification and classification from RGB, Thermal and Multispectral images of Industrial facilities roofs. The idea is to recognize objects from a reference image, extrapolate and analizying them in order to identify if a change or a damage occur over time. The final idea is the damage/change classification in order to gather a full dataset of the industrial facility’s roof.
Development of a Payload Dispenser for Lunar Exploration Rovers
Supervisor: Prof. Marcello Chiaberge, Giacomo Franchini, Gianluca Dara
Skills: Electro-Mechanic Design, CAD (Mandatory) – ROS2 + microROS (Recommended)
Code: 2024PIC15
Topic: EM Designing
Description: The goal of the thesis is to design and development of a payload dispenser prototype which will be able to release different types of payloads onto the surface autonomously. Design and development of dispenser interfaces with the rover. Analysis of the data collected during mission simulations, in order to assess the performance and payload positioning accuracy.
Development of a 2DOF Traction+Steering Wheel Corner for Outdoor Rovers
Supervisor: Prof. Marcello Chiaberge, Giacomo Franchini, Gianluca Dara
Skills: Electro-Mechanic Design, CAD, FEM (Mandatory)
Code: 2024PIC16
Topic: EM Designing
Description: This master thesis focuses on the innovative development of an integrated assembly unit that combines traction and steering functionalities for an outdoor rover platform. By seamlessly integrating these components into a single unit, the research aims to enhance the rover’s maneuverability, stability, and overall performance in challenging terrains.
Reference e-mail: giacomo.franchini@polito.it
Fault Injection in Normalizing Flow Models for Space Applications
Supervisor: Prof. Marcello Chiaberge, Carlo Cena
Skills: Python, Deep Learning (Mandatory) – Git, Time Series (Recommended)
Code: 2024PIC17
Topic: Deep Learning Space
Sensor fusion as a solution to the Kidnapped Robot Problem
Supervisor: Prof. Chiaberge, Ostuni, Eirale, Ambrosio, Boretti
Skills: C++, Python, ROS2, Statistics
Code: 2023PIC01
Description: In robotics, the kidnapped robot problem is the situation where an autonomous robot in operation is carried to an arbitrary location. It is currently one of the most difficult and unsolved problems in the robotics field. This thesis project aims to develop a system able to relocalize the robot by exploiting several sensors (cameras, LiDAR, IMUs, Encoders, etc.) and techniques (traditional methods and learning-based) to solve the kidnapped robot problem.
Complex environment exploration
Supervisor: Prof. Chiaberge, Eirale, Boretti, Martini
Skills: C++/Python, ROS2
Code: 2023PIC02
Description: In order for the robotic platform to properly operate, the knowledge of the working environment is a fundamental aspect. Often this information is not previously provided and could be difficult or dangerous to retrieve. This thesis aims to develop a policy-based agent to fully explore a complex, unknown environment.
Edge Novelty Recognition
Supervisor: Prof. Chiaberge, Albertin, Dara
Skills: C, Python
Code: 2023PIC03
Description: Novelty recognition is a “parallel branch” of predictive maintenance that recognizes novelties from the trained data. It can be useful when no historical data about failures is collected (e.g., a new machine). The thesis concerns the study and development of a novelty detection algorithm to be deployed in an embedded device for predictive maintenance purposes.
Terrain Traversability Analysis for (Planetary Exploration) Rovers
Supervisor: Prof. Chiaberge, Franchini, Messina
Skills: C++/Python, ROS2
Code: 2023PIC04
Description: Novelty recognition is a “parallel branch” of predictive maintenance that recognizes novelties from the trained data. It can be useful when no historical data about failures is collected (e.g., a new machine). The thesis concerns the study and development of a novelty detection algorithm to be deployed in an embedded device for predictive maintenance purposes.
Deep Computer Vision for Social Robotics
Supervisor: Prof. Chiaberge, Angarano, Martini, Eirale, Boretti
Skills: Python, Deep Learning, Linear Algebra
Code: 2023PIC05
Description: Human-Robot Interaction (HRI) is a fundamental component of intelligent robots for well-being which aims to live beside us in the society. However, the nature of HRI can be different according to the context and the human behaviour. This thesis aims at investigating Deep Learning based robotic perception methods for extracting environmental features, in order to build a semantic representation of the human condition (available to interact/busy) and predict the best way of interacting with him/her (visually, talking, physically).
Navigation and Obstacle Avoidance in Vineyards
Supervisor: Prof. Chiaberge, Martini, Mazzara, Ostuni
Skills: Python, Deep Learning, ROS2
Code: 2023PIC06
Description: Low-cost position-agnostic navigation is the solution to GPS-denied conditions in outdoor precision agriculture applications. The two main tasks of a mobile robot in vineyard rows are following the plant rows and avoiding obstacles (boxes, people, etc.) on its trajectory. Multi-class semantic segmentation and multi-objective control strategy can be combined to control the robot’s motion and obtain the resulting final behavior. This thesis aims to improve existing visual-based navigation for vineyard rows.
Industrial Thesis
Optimised time domain response using motion planning innovative strategy
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: Python, System Control Theory
Code: 2023PIC07
Description: The motion systems provide different response while submitted at diverse motion profiles (stroke, acceleration, speed, jerk, snap). The level of vibration and energy during motion change as a function of the those parameters inducing a different response of its elements (cables, mechanical parts, etc.). The goal of the thesis is to identify possible “strategies” (motion profile, frequency rejection technics, learning algorithm…) which limit the energy induced and optimise the motion systems responses.
Heat flow management in motion systems for accuracy optimisation
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: CAD modelling
Code: 2023PIC08
Description: The accuracy of motion systems is affected by the thermal transient and gradient during operation. Those elements generate a “non linear” distortion of the structure which create accuracy losses at machine level. The capability to reduce or avoid such effect is then a key element to improve the motion systems performances. The goal of the thesis is to identify possible strategy of thermal management and/or evacuation (e.g. heat pipe) to minimize thermal gradient and transient of the systems during operation from cold to steady state condition.
Apply AI technics to optimise motion systems feed-forward
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: Python, System Control Theory
Code: 2023PIC09
Description: The motion systems performances are closely related to the feed-forward capabilities. The systems characteristics are not “always” constant and linear. For instance, the moving “apparent” mass of dynamic cable changes as a function of the position. In addition, non linearity, such as friction, could present different responses over time and based on specific positions and boundary conditions. The goal of the thesis is to analyse in details those behaviours and apply Artificial Intelligence technics to optimised and/or adapt the feed-forward of our advanced motion systems.
Sensor fusion as a solution to the Kidnapped Robot Problem
Supervisor: Prof. Chiaberge, Ostuni, Eirale, Ambrosio, Boretti
Skills: C++, Python, ROS2, Statistics
Code: 2023PIC01
Description: In robotics, the kidnapped robot problem is the situation where an autonomous robot in operation is carried to an arbitrary location. It is currently one of the most difficult and unsolved problems in the robotics field. This thesis project aims to develop a system able to relocalize the robot by exploiting several sensors (cameras, LiDAR, IMUs, Encoders, etc.) and techniques (traditional methods and learning-based) to solve the kidnapped robot problem.
Complex environment exploration
Supervisor: Prof. Chiaberge, Eirale, Boretti, Martini
Skills: C++/Python, ROS2
Code: 2023PIC02
Description: In order for the robotic platform to properly operate, the knowledge of the working environment is a fundamental aspect. Often this information is not previously provided and could be difficult or dangerous to retrieve. This thesis aims to develop a policy-based agent to fully explore a complex, unknown environment.
Edge Novelty Recognition
Supervisor: Prof. Chiaberge, Albertin, Dara
Skills: C, Python
Code: 2023PIC03
Description: Novelty recognition is a “parallel branch” of predictive maintenance that recognizes novelties from the trained data. It can be useful when no historical data about failures is collected (e.g., a new machine). The thesis concerns the study and development of a novelty detection algorithm to be deployed in an embedded device for predictive maintenance purposes.
Terrain Traversability Analysis for (Planetary Exploration) Rovers
Supervisor: Prof. Chiaberge, Franchini, Messina
Skills: C++/Python, ROS2
Code: 2023PIC04
Description: Novelty recognition is a “parallel branch” of predictive maintenance that recognizes novelties from the trained data. It can be useful when no historical data about failures is collected (e.g., a new machine). The thesis concerns the study and development of a novelty detection algorithm to be deployed in an embedded device for predictive maintenance purposes.
Deep Computer Vision for Social Robotics
Supervisor: Prof. Chiaberge, Angarano, Martini, Eirale, Boretti
Skills: Python, Deep Learning, Linear Algebra
Code: 2023PIC05
Description: Human-Robot Interaction (HRI) is a fundamental component of intelligent robots for well-being which aims to live beside us in the society. However, the nature of HRI can be different according to the context and the human behaviour. This thesis aims at investigating Deep Learning based robotic perception methods for extracting environmental features, in order to build a semantic representation of the human condition (available to interact/busy) and predict the best way of interacting with him/her (visually, talking, physically).
Navigation and Obstacle Avoidance in Vineyards
Supervisor: Prof. Chiaberge, Martini, Mazzara, Ostuni
Skills: Python, Deep Learning, ROS2
Code: 2023PIC06
Description: Low-cost position-agnostic navigation is the solution to GPS-denied conditions in outdoor precision agriculture applications. The two main tasks of a mobile robot in vineyard rows are following the plant rows and avoiding obstacles (boxes, people, etc.) on its trajectory. Multi-class semantic segmentation and multi-objective control strategy can be combined to control the robot’s motion and obtain the resulting final behavior. This thesis aims to improve existing visual-based navigation for vineyard rows.
Industrial Thesis
Optimised time domain response using motion planning innovative strategy
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: Python, System Control Theory
Code: 2023PIC07
Description: The motion systems provide different response while submitted at diverse motion profiles (stroke, acceleration, speed, jerk, snap). The level of vibration and energy during motion change as a function of the those parameters inducing a different response of its elements (cables, mechanical parts, etc.). The goal of the thesis is to identify possible “strategies” (motion profile, frequency rejection technics, learning algorithm…) which limit the energy induced and optimise the motion systems responses.
Heat flow management in motion systems for accuracy optimisation
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: CAD modelling
Code: 2023PIC08
Description: The accuracy of motion systems is affected by the thermal transient and gradient during operation. Those elements generate a “non linear” distortion of the structure which create accuracy losses at machine level. The capability to reduce or avoid such effect is then a key element to improve the motion systems performances. The goal of the thesis is to identify possible strategy of thermal management and/or evacuation (e.g. heat pipe) to minimize thermal gradient and transient of the systems during operation from cold to steady state condition.
Apply AI technics to optimise motion systems feed-forward
Supervisor: Prof. Chiaberge, Albertin, Lacagnina (Etel supervisor)
Collaboration: Etel SA
Skills: Python, System Control Theory
Code: 2023PIC09
Description: The motion systems performances are closely related to the feed-forward capabilities. The systems characteristics are not “always” constant and linear. For instance, the moving “apparent” mass of dynamic cable changes as a function of the position. In addition, non linearity, such as friction, could present different responses over time and based on specific positions and boundary conditions. The goal of the thesis is to analyse in details those behaviours and apply Artificial Intelligence technics to optimised and/or adapt the feed-forward of our advanced motion systems.