Till Hielscher — accepted AI-conference papers

2025

Interactive Expressive Motion Generation Using Dynamic Movement Primitives

Till Hielscher, Andreas Bulling, Kai O. Arras

IROS 2025

Our goal is to enable social robots to interact autonomously with humans in a realistic, engaging, and expressive manner. The 12 Principles of Animation are a well-established framework animators use to create movements that make characters appear convincing, dynamic, and emotionally expressive. Thi

Cited by 2 Source ↗Scholar ↗

2024

Efficient Context-Aware Model Predictive Control for Human-Aware Navigation

Elisa Stefanini, Luigi Palmieri, Andrey Rudenko, Till Hielscher, Timm Linder, Lucia Pallottino

RA-L 2024

With the goal of creating efficient human-aware robot navigation systems, we present a Context-aware Model Predictive Control (MPC) formulation designed specifically for dynamic and crowded environments. State-of-the-art approaches use mainly geometric information and predictions of human motion, th

Cited by 18 Source ↗Scholar ↗