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From imitation to self-exploration: Learning object manipulation skills
Robotics Seminar Series. Second Session 2022 – 1st April 2022. Speaker: Seungsu Kim
Understanding Capsule Networks: will they overcome Convolutional Neural Networks?
Robotics Seminar Series. First Session 2022 – 17th March 2022. Speaker: Riccardo Renzulli
Welcome to Riccardo Renzulli
Riccardo is pursuing his PhD in University of Turin, Italy, and will stay with us for 6 months for an Erasmus Trainee project.
Master Thesis on “Reinforcement Learning for deformable object manipulation”
This thesis will focus on how to apply Reinforcement Learning methods for solving deformable object manipulation tasks.
Defense of doctoral thesis “Towards Robust 6-DoF Multi-Finger Grasping in Clutter with Explicit Scene Understanding”, M.Sc.(Tech.) Jens Lundell
Defense of doctoral thesis. 25.02.2022, 12:00-15:00. Doctoral candidate: M.Sc. Jens Lundell
Building a quadruped robot for reinforcement learning research
The goal of this project is to build the open-sourced quadruped robot called Real-Ant. To start with, the 3D models and the codes for the microcontroller as well as serial port communication are available for the robot. So the first task will be to print the components, assemble the robot and test the available codes on the robot. Then in the second stage, we will modify the robot to make it more robust so that it can tolerate mild shocks and can be run for an extended period of time without the need of tightening the screws or replacing the components. For example, we can think of adding soft legs, cushions etc. to protect the belly and the actuators. In addition, we can also simplify the design so that it can be repaired quickly if required.
Welcome to Matti Pekkanen
We welcome Matti as a new PhD Candidate in the group.
Welcome to Daulet Baimukashe
We welcome Daulet as a new PhD Candidate in the group.
Master Thesis on the Co-Adaptation of Robots
The goal of this Master thesis is to develop simulation tools necessary to evaluate co-adaptation techniques, and to develop new approaches for learning the behaviour and design of robots using deep learning and deep reinforcement learning.
Master Thesis on “Safe Constrained Differential Dynamic Programming”
In this thesis, an extensive investigation of constrained DDP methods will be performed and the major selected ones will be implemented in simulation environment for trajectory optimizations of different robots such as a simple point robot, 2D car-like robot, 3D quadrotor robot and cart-pole system. In this context, the methods will be compared in terms of convergence speed, computational complexity, sensitivity to initializations and parameter selections.