Robotic grasping has been studied for more than 30 years, but it is still a challenging field. Today, most robotic grippers are rigid, making it hard for them to grasp and handle irregularly shaped objects that are delicate and easily deformed such as a compact disc, an egg, or an empty plastic cup. To tackle […]

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 […]