Martin Feick — accepted AI-conference papers

2024

Bridging the Gap to Natural Language-based Grasp Predictions through Semantic Information Extraction

Niko Kleer, Martin Feick, Amr Gomaa, Michael Feld, Antonio Krüger

IROS 2024poster

Enabling multi-fingered robots to choose an appropriate grasp on an object from natural language instructions poses great difficulties for such systems. The diversity, imprecision, and limited information contained in the language make this task particularly challenging. However, speech serves human…

Cited by 0 Source ↗Scholar ↗

2022

Leveraging Publicly Available Textual Object Descriptions for Anthropomorphic Robotic Grasp Predictions

Niko Kleer, Martin Feick, Michael Feld

IROS 2022poster

Robotic systems using anthropomorphic end-effectors face tremendous challenges choosing a suitable pose for grasping an object. The fact that the choice of a grasp is influenced by the physical properties of an object, the intended task, and the environment results in a considerable amount of variab…

Cited by 4 Source ↗Scholar ↗