Learning Interpretable Reduced-order Models for Jumping Quadrupeds

This work introduces a novel methodology for the development of interpretable reduced-order dynamic models specifically tailored for jumping quadruped robots. Leveraging Symbolic Regression combined with autoencoder neural networks, the framework autonomously derives symbolic equations from data and fundamental physics principles capturing the...

Towards Corrective Deep Imitation Learning in Data Intensive Environments: Helping robots to learn faster by leveraging human knowledge

Interactive imitation learning refers to learning methods where a human teacher interacts with an agent during the learning process providing feedback to improve its behaviour. This type of learning may be preferable with respect to reinforcement learning techniques when dealing with real-world problems. This fact is especially true in the case...

Single Shot Learning of the Stiffness Distribution of a Soft Object

With robotics rapidly expanding towards new user-oriented applications, such as agriculture, households, and classrooms, new tasks and new requirements arise. One of the ongoing and unsolved problems that comes with the unpredictable new environments robots find themselves in, is soft object manipulation. Without assuming the handled object to...

BedBasedEcho

The core challenge of the BedBasedEcho BEP project is to create an algorithm to find the heart, and apply it on a robotic echocardiography solution. The team has found multiple complex solutions that are related to this problem, and has extracted useful information from these solutions to apply to this problem. However, some of these complex...

Learning to Control Multi- Dimensional Autonomous Agents using Hebbian Learning: A Global Reward Approach

The novelty-raahn algorithm has been shown to effectively learn a desired behavior from raw inputs by connecting an autoencoder with a Hebbian network. Hebbian learning is compelling for its biological plausibility and simplicity. It changes the weight of a connection based only on the activations of neurons it connects, and can effectively...

Fast Learning of Human Interaction Behavior: Learning Patterns of User Interaction on the LEA Robot

This thesis describes three different predictive frameworks which are applied to improve the interaction behavior between a robot and their users. Machine learning is used to train the frameworks. The training data contains information obtained from one user over a period of approximately 3 to 30 days. This work is applied to the LEA robot: an...

Learning Autonomous Grasping Strategies for a Care Robot: A Machine Learning approach

Autonomous grasping is a key requisite for the autonomy of robots.<br/>However, grasping of unknown objects in domestic environments is difficult due to the presence of unpredictability and clutter.<br/>In this paper, a novel algorithm capable of finding an unobstructed grasping pose on unknown regular object shapes in cluttered environments is...