Knowledge Technology Awarded at IJCNN'24

7 July 2024

Excellent late-breaking news from the IEEE IJCNN conference for us. 2024 Runner-Up Best Student Paper Award at IJCNN Yokohama! Congratulations on winning the 2024 Runner-Up Best Student Paper Award at the IEEE International Joint Conference on Neural Networks, Yokohama, Japan.

The topic is on domain adaption and sim-to-real transfer on our humanoid neuro-robotic NICO. Here is more information about the paper:

Authors: Connor Gaede, Jan-Gerrit Habekost, Stefan Wermter

Title: Domain Adaption as Auxiliary Task for Sim-to-Real Transfer in Vision-based Neuro-Robotic Control

Abstract: Architectures for vision-based robot manipulation often utilize separate domain adaption models to allow sim-to-real transfer and an inverse kinematics solver to allow the actual policy to operate in Cartesian space. We present a novel end-to-end visuomotor architecture that combines domain adaption and inherent inverse kinematics in one model. Using the same latent encoding, it jointly learns to reconstruct canonical simulation images from randomized inputs and to predict the corresponding joint angles that minimize the Cartesian error towards a depicted target object via differentiable forward kinematics. We evaluate our model in a sim-to-real grasping experiment with the NICO humanoid robot by comparing different randomization and adaption conditions both directly and with additional real-world finetuning. Our combined method significantly increases the resulting accuracy and allows a finetuned model to reach a success rate of 80.30%, outperforming a real-world model trained with six times as much real data.

You can access the full paper here.