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20232026
most citedTowards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.RO2026

Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance

Miroslav Krupa, Miroslav Cibula, Kristína Malinovská

Trajectory planning is a fundamental problem in robotics, requiring the generation of collision-free and efficient trajectories in a potentially complex environment. While sampling…

cs.RO2025

Toward an Interaction-Centered Approach to Robot Trustworthiness

Carlo Mazzola, Hassan Ali, Kristína Malinovská +1

As robots get more integrated into human environments, fostering trustworthiness in embodied robotic agents becomes paramount for an effective and safe human-robot interaction (HRI…

cs.RO2025

Examining the legibility of humanoid robot arm movements in a pointing task

Andrej Lúčny, Matilde Antonj, Carlo Mazzola +5

Human--robot interaction requires robots whose actions are legible, allowing humans to interpret, predict, and feel safe around them. This study investigates the legibility of huma…

cs.RO20251 cited

Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN

Miroslav Cibula, Kristína Malinovská, Matthias Kerzel

Trajectory planning in robotics is understood as generating a sequence of joint configurations that will lead a robotic agent, or its manipulator, from an initial state to the desi…

cs.NE2024

QuasiNet: a neural network with trainable product layers

Kristína Malinovská, Slavomír Holenda, Ľudovít Malinovský

Classical neural networks achieve only limited convergence in hard problems such as XOR or parity when the number of hidden neurons is small. With the motivation to improve the suc…

cs.RO2023

Robot at the Mirror: Learning to Imitate via Associating Self-supervised Models

Andrej Lucny, Kristina Malinovska, Igor Farkas

We introduce an approach to building a custom model from ready-made self-supervised models via their associating instead of training and fine-tuning. We demonstrate it with an exam…