Jan Seyler — accepted AI-conference papers

2025

Synthesizing Depowdering Trajectories for Robot Arms using Deep Reinforcement Learning

Maximilian Maurer, Simon Seefeldt, Jan Seyler, Shahram Eivazi

ICRA 2025

Research into robotics applications of deep reinforcement learning (DRL) has increasingly been focussed on learning precise object manipulation and trajectory planning. Extending these tasks to continuous robot-object interactions with the surface of complex geometries remains an open problem. In th

Cited by 1 Source ↗Scholar ↗

2023

Exploiting Spatio-Temporal Human-Object Relations Using Graph Neural Networks for Human Action Recognition and 3D Motion Forecasting

Dimitrios Lagamtzis, Fabian Schmidt, Jan Seyler, Thao Dang, Steffen Schober

IROS 2023poster

Human action recognition and motion forecasting is becoming increasingly successful, in particular with utilizing graphs. We aim to transfer this success into the context of industrial Human-Robot Collaboration (HRC), where humans work closely with robots and interact with workpieces in defined work…

Cited by 5 Source ↗Scholar ↗