In robotic manipulation, robots are required to interact with, and adapt to, unknown environments and objects. In order to successfully accomplish these tasks, robots need to identify various properties of the objects to be handled. For these reasons, identifying object models that can represent the properties of objects has become a crucial issue in robotics. […]

Safe Model Predictive Control (Safe MPC) aims to ensure that a physical system’s safety constraints are satisfied with high probability. Our research is on extending constrained MPC methods to cope with probabilistic safety constraints. We further research modeling uncertainty of dynamics to ensure safe exploration when combined with safety constraints learned in simulation, and learning powerful data-efficient surrogate models for complex dynamics.

Hypermaps aim at improving how robots manage data about the environment they inhabit.

Tactile sensors have been primarily used to improve the agile manipulation capabilities of the robots. As opposed to previous efforts, the aim of this research is to be able to develop a reproducible and open-source multi-point tactile sensor array. In doing so, we aim to develop a sensor array that can provide an array of […]

Humans possess the innate ability to navigate from one location to another and orient themselves to avoid getting lost in the process. Studies from rodents and other model species have provided unprecedented insights into the potential neural correlates of mammalian spatial cognition. Yet, those correlates have so far revealed little about applicable principles of navigational […]

In addition to transportation comfort and efficiency, autonomous vehicles provide a vital improvement in traffic safety by minimizing impact of human factor. In this project, the data- and model-based approaches will be combined to develop a safety-oriented decision-making algorithm for autonomous driving systems. The main assessment criteria for the vehicle performance actuation is traffic safety, […]