6 citations · 11 across the 5 of their papers we have counts for
5 papers
SafeFlowMPC: Predictive and Safe Trajectory Planning for Robot Manipulators with Learning-based Policies
Thies Oelerich, Gerald Ebmer, Christian Hartl-Nesic +1
The emerging integration of robots into everyday life brings several major challenges. Compared to classical industrial applications, more flexibility is needed in combination with…
Incremental Language Understanding for Online Motion Planning of Robot Manipulators
Mitchell Abrams, Thies Oelerich, Christian Hartl-Nesic +2
Human-robot interaction requires robots to process language incrementally, adapting their actions in real-time based on evolving speech input. Existing approaches to language-guide…
BoundPlanner: A convex-set-based approach to bounded manipulator trajectory planning
Thies Oelerich, Christian Hartl-Nesic, Florian Beck +1
Online trajectory planning enables robot manipulators to react quickly to changing environments or tasks. Many robot trajectory planners exist for known environments but are often…
Model Predictive Trajectory Planning for Human-Robot Handovers
Thies Oelerich, Christian Hartl-Nesic, Andreas Kugi
This work develops a novel trajectory planner for human-robot handovers. The handover requirements can naturally be handled by a path-following-based model predictive controller, w…
BoundMPC: Cartesian path following with error bounds based on model predictive control in the joint space
Thies Oelerich, Florian Beck, Christian Hartl-Nesic +1
This work introduces the BoundMPC strategy, an innovative online model-predictive path-following approach for robot manipulators. This joint-space trajectory planner allows the fol…