Information-driven Affordance Discovery for Efficient Robotic Manipulation

no code implementations • 6 May 2024 • Pietro Mazzaglia, Taco Cohen, Daniel Dijkman

Robotic affordances, providing information about what actions can be taken in a given situation, can aid robotic manipulation.

Neural 5G Indoor Localization with IMU Supervision

no code implementations • 15 Feb 2024 • Aleksandr Ermolov, Shreya Kadambi, Maximilian Arnold, Mohammed Hirzallah, Roohollah Amiri, Deepak Singh Mahendar Singh, Srinivas Yerramalli, Daniel Dijkman, Fatih Porikli, Taesang Yoo, Bence Major

We propose practical algorithms for IMU double integration and training of the localization system.

Indoor Localization Position

Deconfounded Imitation Learning

no code implementations • 4 Nov 2022 • Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan

Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent.

Imitation Learning

Modality-Agnostic Topology Aware Localization

no code implementations • NeurIPS 2021 • Farhad Ghazvinian Zanjani, Ilia Karmanov, Hanno Ackermann, Daniel Dijkman, Simone Merlin, Max Welling, Fatih Porikli

This work presents a data-driven approach for the indoor localization of an observer on a 2D topological map of the environment.

Indoor Localization

WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labels

no code implementations • 31 May 2021 • Ilia Karmanov, Farhad G. Zanjani, Simone Merlin, Ishaque Kadampot, Daniel Dijkman

Our second contribution is two weakly supervised losses that map this latent space into a Cartesian map, resulting in meter-accuracy position results.

Dimensionality Reduction Position