Christian Merz

Enrico Marchesini, Benjamin Donnot, Constance Crozier et al.

Reinforcement learning (RL) can provide adaptive and scalable controllers essential for power grid decarbonization. However, RL methods struggle with power grids' complex dynamics, long-horizon goals, and hard physical constraints. For these reasons, we present RL2Grid, a benchmark designed in collaboration with power system operators to accelerate progress in grid control and foster RL maturity. Built on RTE France's power simulation framework, RL2Grid standardizes tasks, state and action spaces, and reward structures for a systematic evaluation and comparison of RL algorithms. Moreover, we integrate operational heuristics and design safety constraints based on human expertise to ensure alignment with physical requirements. By establishing reference performance metrics for classic RL baselines on RL2Grid's tasks, we highlight the need for novel methods capable of handling real systems and discuss future directions for RL-based grid control.

Christian Merz, Lukas Schach, Marie Luisa Fiedler et al.

This paper introduces an unobtrusive in-situ measurement method to detect user behavior changes during arbitrary exposures in XR systems. Here, such behavior changes are typically associated with the Proteus effect or bodily affordances elicited by different avatars that the users embody in XR. We present a biometric user model based on deep metric similarity learning, which uses high-dimensional embeddings as reference vectors to identify behavior changes of individual users. We evaluate our model against two alternative approaches: a (non-learned) motion analysis based on central tendencies of movement patterns and subjective post-exposure embodiment questionnaires frequently used in various XR exposures. In a within-subject study, participants performed a fruit collection task while embodying avatars of different body heights (short, actual-height, and tall). Subjective assessments confirmed the effective manipulation of perceived body schema, while the (non-learned) objective analyses of head and hand movements revealed significant differences across conditions. Our similarity learning model trained on the motion data successfully identified the elicited behavior change for various query and reference data pairings of the avatar conditions. The approach has several advantages in comparison to existing methods: 1) In-situ measurement without additional user input, 2) generalizable and scalable motion analysis for various use cases, 3) user-specific analysis on the individual level, and 4) with a trained model, users can be added and evaluated in real time to study how avatar changes affect behavior.