Angela Schoellig

Martin Schuck, Jan Brüdigam, Sandra Hirche et al.

Handling orientations of robots and objects is a crucial aspect of many applications. Yet, ever so often, there is a lack of mathematical correctness when dealing with orientations, especially in learning pipelines involving, for example, artificial neural networks. In this paper, we investigate reinforcement learning with orientations and propose a simple modification of the network's input and output that adheres to the Lie group structure of orientations. As a result, we obtain an easy and efficient implementation that is directly usable with existing learning libraries and achieves significantly better performance than other common orientation representations. We briefly introduce Lie theory specifically for orientations in robotics to motivate and outline our approach. Subsequently, a thorough empirical evaluation of different combinations of orientation representations for states and actions demonstrates the superior performance of our proposed approach in different scenarios, including: direct orientation control, end effector orientation control, and pick-and-place tasks.

Hannah Schieber, Dominik Frischmann, Victor Schaack et al.

Mobile reconstruction has the potential to support time-critical tasks such as tele-guidance and disaster response, where operators must quickly gain an accurate understanding of the environment. Full high-fidelity scene reconstruction is computationally expensive and often unnecessary when only specific points of interest (POIs) matter for timely decision making. We address this challenge with CoRe-GS, a semantic POI-focused extension of Gaussian Splatting (GS). Instead of optimizing every scene element uniformly, CoRe-GS first produces a fast segmentation-ready GS representation and then selectively refines splats belonging to semantically relevant POIs detected during data acquisition. This targeted refinement reduces training time to 25\% compared to full semantic GS while improving novel view synthesis quality in the areas that matter most. We validate CoRe-GS on both real-world (SCRREAM) and synthetic (NeRDS 360) datasets, demonstrating that prioritizing POIs enables faster and higher-quality mobile reconstruction tailored to operational needs.

Karime Pereida, Angela Schoellig

Robots and automated systems are increasingly being introduced to unknown and dynamic environments where they are required to handle disturbances, unmodeled dynamics, and parametric uncertainties. Robust and adaptive control strategies are required to achieve high performance in these dynamic environments. In this paper, we propose a novel adaptive model predictive controller that combines model predictive control (MPC) with an underlying $\mathcal{L}_1$ adaptive controller to improve trajectory tracking of a system subject to unknown and changing disturbances. The $\mathcal{L}_1$ adaptive controller forces the system to behave in a predefined way, as specified by a reference model. A higher-level model predictive controller then uses this reference model to calculate the optimal reference input based on a cost function, while taking into account input and state constraints. We focus on the experimental validation of the proposed approach and demonstrate its effectiveness in experiments on a quadrotor. We show that the proposed approach has a lower trajectory tracking error compared to non-predictive, adaptive approaches and a predictive, non-adaptive approach, even when external wind disturbances are applied.