6 accepted papers
The objective of constrained motion planning is to connect start and goal configurations while satisfying task-specific constraints. Motion planning becomes inefficient or infeasible when the configurations lie in disconnected regions, known as essentially mutually disconnected components (EMDs). Co…
With the growing employment of learning algorithms in robotic applications, research on reinforcement learning for bipedal locomotion has become a central topic for humanoid robotics. While recently published contributions achieve high success rates in locomotion tasks, scarce attention has been dev…
This paper presents an active control strategy for lower limb exoskeletons by proposing an end-to-end framework employing deep reinforcement learning (DRL) to enable smooth transitions between different movement patterns. The majority of existing methods in exoskeleton literature employ finite state
This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to
In this paper, we present a method to engage measurement uncertainties with the probabilistic robustness to one system uncertainty measure. Providing a metric indicating the potential occurrence of dangerous situations is highly essential for safety-critical robot applications. Due to the difficulty…
In this paper, we present an approach for quantifying the propagated uncertainty of robot systems in an online and data-driven manner. Especially in Human-Robot Collaboration, keeping track of the safety compliance during run time is essential: Misclassifying dangerous situations as safe might resul…