5 accepted papers
Learning-based controllers are often purposefully kept out of real-world applications due to concerns about their safety and reliability. We explore how state-of-the-art world models in Model-Based Reinforcement Learning can be utilized beyond the training phase to ensure a deployed policy only oper
This paper firstly presents our optimal path planning algorithm within a 2\mathrm{D}2\mathrm{D} non-convex, polytopic region defined as a sequence of connected convex polytopes. The path is a B-spline curve but being parametrized with its equivalent Bézier representation. By doing this, the local co…
In this paper, a Deep Neural Network is trained using Reinforcement Learning in order to drift on arbitrary trajectories which are defined by a sequence of waypoints. In a first step, a highly accurate vehicle simulation is used for the training process. Then, the obtained policy is refined and vali…
Developing reusable software for mobile robots is still challenging. Even more so for swarm robots, despite the desired simplicity of the robot controllers. Prototyping and experimenting are difficult due to the multi-robot setting and often require robot-robot communication. Also, the diversity of…
We propose a 2D path planning algorithm in a non-convex workspace defined as a sequence of connected convex polytopes. The reference path is parameterized as a B-spline curve, which is guaranteed to entirely remain within the workspace by exploiting the local convexity property and by formulating li…