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Enhancement of 3D Camera Synthetic Training Data with Noise Models
The goal of this paper is to assess the impact of noise in 3D camera-captured data by modeling the noise of the imaging process and …
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To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills
Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment …
Cite DOI ICJNN 2023
Clarifying the Half Full or Half Empty Question: Multimodal Container Classification
Multimodal integration is a key component of allowing robots to perceive the world. Multimodality comes with multiple challenges that …
PDF DOI ICANN 2023
CycleIK: Neuro-inspired Inverse Kinematics
The paper introduces CycleIK, a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) …
PDF DOI ICANN 2023
Neural Field Conditioning Strategies for 2D Semantic Segmentation
Neural fields are neural networks which map coordinates to a desired signal. When a neural field should jointly model multiple signals, …
PDF DOI ICANN 2023
Novel Synthetic Data Tool for Data-Driven Cardboard Box Localization
Application of neural networks in industrial settings, such as automated factories with bin-picking solutions requires costly …
DOI ICANN 2023
QuasiNet: a Neural Network with Trainable Product Layers
Classical neural networks achieve only limited convergence in hard problems such as XOR or parity when the number of hidden neurons is …
DOI ICANN 2023
Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition
While Automatic Speech Recognition (ASR) models have shown significant advances with the introduction of unsupervised or …
PDF DOI ICANN 2023
Robot at the Mirror: Learning to Imitate via Associating Self-Supervised Models
We introduce an approach to building a custom model from ready-made self-supervised models via their associating instead of training …
DOI ICANN 2023
Safe Reinforcement Learning in a Simulated Robotic Arm
Reinforcement learning (RL) agents need to explore their environments in order to learn optimal policies. In many environments and …
Cite DOI ICANN 2023