16 citations · 16 across the 5 of their papers we have counts for
5 papers
Can Context Bridge the Reality Gap? Sim-to-Real Transfer of Context-Aware Policies
Marco Iannotta, Yuxuan Yang, Johannes A. Stork +2
Sim-to-real transfer remains a major challenge in reinforcement learning (RL) for robotics, as policies trained in simulation often fail to generalize to the real world due to disc…
The First WARA Robotics Mobile Manipulation Challenge -- Lessons Learned
David Cáceres Domínguez, Marco Iannotta, Abhishek Kashyap +21
The first WARA Robotics Mobile Manipulation Challenge, held in December 2024 at ABB Corporate Research in Västerås, Sweden, addressed the automation of task-intensive and repetitiv…
On the Fly Adaptation of Behavior Tree-Based Policies through Reinforcement Learning
Marco Iannotta, Johannes A. Stork, Erik Schaffernicht +1
With the rising demand for flexible manufacturing, robots are increasingly expected to operate in dynamic environments where local -- such as slight offsets or size differences in…
Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees
Marco Iannotta, David Cáceres Domínguez, Johannes A. Stork +2
Integrating the heterogeneous controllers of a complex mechanical system, such as a mobile manipulator, within the same structure and in a modular way is still challenging. In this…
A Stack-of-Tasks Approach Combined with Behavior Trees: a New Framework for Robot Control
David Cáceres Domínguez, Marco Iannotta, Johannes A. Stork +2
Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this appro…