12 accepted papers
Model Predictive Control (MPC) is among the most widely adopted and reliable methods for robot control, relying critically on an accurate dynamics model. However, existing dynamics models used in the gradient-based MPC are limited by computational complexity and state representation. To address this…
Fully autonomous vehicles promise enhanced safety and efficiency. However, ensuring reliable operation in challenging corner cases requires control algorithms capable of performing at the vehicle limits. We address this requirement by considering the task of autonomous racing and propose solving it
Trajectory planning under kinodynamic constraints is fundamental for advanced robotics applications that require dexterous, reactive, and rapid skills in complex environments. These constraints, which may represent task, safety, or actuator limitations, are essential for ensuring the proper function…
Manipulating Deformable Linear Objects (DLOs) is a challenging task for a robotic system due to their unpredictable configuration, high-dimensional state space and complex nonlinear dynamics. This paper presents a framework addressing the manipulation of DLOs, specifically targeting the model-based
Velocity estimation is of great importance in autonomous racing. Still, existing solutions are characterized by limited accuracy, especially in the case of aggressive driving or poor generalization to unseen road conditions. To address these issues, we propose to utilize Unscented Kalman Filter (UKF…
Deep Reinforcement Learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different emb…
Collaborative robots (cobots) are a promising technology for frontline workers in industry. They can support tasks that cannot be fully automated but are repetitive, fatiguing, boring, or dangerous for humans. Although cobots are explicitly designed to work with humans, they remain primarily non-int…
While manipulating rigid objects is an extensively explored research topic, deformable linear object (DLO) manipulation seems significantly underdeveloped. A potential reason for this is the inherent difficulty in describing and observing the state of the DLO as its geometry changes during manipulat…
Haptic sensing has recently been used effectively for legged robot localization in extreme scenarios where cam-eras and LiDAR might fail, such as dusty mines and foggy sewers. However, existing haptic sensing mainly relies on supervised classification, with training and evaluation executed over expl…
Mobile robots are becoming very popular in real-world outdoors applications, where there are many challenges in robot control and perception. One of the most critical problems is to characterise the terrain traversed by the robot. This knowledge is indispensable for optimal terrain negotiation. Curr…
Legged robots have the potential to traverse diverse and rugged terrain. To find a safe and efficient navigation path and to carefully select individual footholds, it is useful to be able to predict properties of the terrain ahead of the robot. In this letter, we propose a method to collect data fro
One of the major goals for mobile robots is to be able to traverse any kind of terrains. A possible way to achieve this goal is by the use of legged robots, as they have increased mobility. However, this would require them to be able to modify their gaits, based on the identification of the terrain…