3 accepted papers
Training recurrent neural networks (RNNs) with standard backpropagation through time (BPTT) can be challenging, especially in the presence of long input sequences. A practical alternative to reduce computational and memory overhead is to perform BPTT repeatedly over shorter segments of the training…
The rise of autonomous driving in everyday life makes efficient and collision-free motion planning more important than ever. However, multi robot applications in highly dynamic environments still pose hard challenges for state-of-the-art motion planners. In this paper, we present a new iteration of…
Safe and efficient trajectory planning for autonomous robots is becoming increasingly important in both industrial applications and everyday life. The demands on a robot which has to react quickly and precisely to changes in cluttered, unknown and dynamic environments are particularly high. Towards