7 papers · 1 filter
A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures
Maximilian Mühlbauer, Bernhard Weber, Sylvain Calinon +3
Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned or perceived uncertainty. While k…
MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation
Markus Knauer, Edoardo Fiorini, Maximilian Mühlbauer +10
Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However, different adaptations benefi…
Discovering Optimal Natural Gaits of Dissipative Systems via Virtual Energy Injection
Korbinian Griesbauer, Davide Calzolari, Maximilian Raff +2
Legged robots offer several advantages when navigating unstructured environments, but they often fall short of the efficiency achieved by wheeled robots. One promising strategy to…
Software for the SpaceDREAM Robotic Arm
Maximilian Mühlbauer, Maxime Chalon, Maximilian Ulmer +1
Impedance-controlled robots are widely used on Earth to perform interaction-rich tasks and will be a key enabler for In-Space Servicing, Assembly and Manufacturing (ISAM) activitie…
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks
Antonin Raffin, Olivier Sigaud, Jens Kober +3
In search of a simple baseline for Deep Reinforcement Learning in locomotion tasks, we propose a model-free open-loop strategy. By leveraging prior knowledge and the elegance of si…
Nonlinear Modes as a Tool for Comparing the Mathematical Structure of Dynamic Models of Soft Robots
Pietro Pustina, Davide Calzolari, Alin Albu-Schäffer +2
Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dyna…