HoloAssembly: Learn an assembly task using HoloLens
One of the most exciting uses of mixed-reality head-mounted devices like the Microsoft HoloLensis to assist a user to learn / perform tasks.The HoloLens sees the world through a depth and a RGB camera, and can map the position of theuser in the real world through the built-in head tracking. It can also provide compelling experienceswhere virtual 3D objects (holograms) are mixed with real ones in the real world. However, the devicedoes not have capabilities yet to reason about what the user is doing, or how they should interact withthe objects around. The goal of this project is to start exploring this area, by building a HoloLensapp to assist the user in a pre-determined assembly task – like assemblying a piece of Ikea furniture.The student will get access to a HoloLens 2 device, and will therefore be able to leverage recentcapabilities like fully articulated hand tracking and eye gaze tracking.
Simulation and parameter estimation for soft robotics
We aim to build fast simulation models of soft deformable materials in order to facilitate soft robot design, or robotic manipulation of elastic objects.
Robotic clay modeling
Robots can perform many tasks today, but can they model clay? In contrast to previous robotic manipulation technique, which mostly deal with hard materials, in this project we will explore robotic manipulation of materials that \emph{flow}: clay, plaster, stucco, etc. Our ultimate goal is to control a robot that can perform sculpting tasks or pottery like the ones in the picture above.
Castle Maker: Simultaneous and collaborative robotic assembly
Assembly is one of the fundamental tasks that robots were designed to do. The standard approach that has been used since the dawn of robotics is to break the assembly procedure into smaller tasks that can be run sequentially. This approach benefits from easy programming and computation. However, the
Deep learning Hololens project in collaboration with Microsoft
Microsoft has proposed two projects related to object pose estimation and interaction with robots. More details in the attached document.

Powered by  SiROP - the academic career network