Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Chatzilygeroudis, Konstantinos"'
Legged robotic systems can play an important role in real-world applications due to their superior load-bearing capabilities, enhanced autonomy, and effective navigation on uneven terrain. They offer an optimal trade-off between mobility and payload
Externí odkaz:
http://arxiv.org/abs/2410.02891
Autor:
Totsila, Dionis, Chatzilygeroudis, Konstantinos, Hadjivelichkov, Denis, Modugno, Valerio, Hatzilygeroudis, Ioannis, Kanoulas, Dimitrios
State-of-the-art sensorimotor learning algorithms offer policies that can often produce unstable behaviors, damaging the robot and/or the environment. Traditional robot learning, on the contrary, relies on dynamical system-based policies that can be
Externí odkaz:
http://arxiv.org/abs/2305.12886
One of today's goals for industrial robot systems is to allow fast and easy provisioning for new tasks. Skill-based systems that use planning and knowledge representation have long been one possible answer to this. However, especially with contact-ri
Externí odkaz:
http://arxiv.org/abs/2212.03570
In real-world environments, robots need to be resilient to damages and robust to unforeseen scenarios. Quality-Diversity (QD) algorithms have been successfully used to make robots adapt to damages in seconds by leveraging a diverse set of learned ski
Externí odkaz:
http://arxiv.org/abs/2210.09918
Autor:
Mayr, Matthias, Hvarfner, Carl, Chatzilygeroudis, Konstantinos, Nardi, Luigi, Krueger, Volker
Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill parameters. One strategy is to learn these parameters by allowing the robot s
Externí odkaz:
http://arxiv.org/abs/2208.01605
Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-trained skills can enable robots to adapt to unforeseen mechanical damages
Externí odkaz:
http://arxiv.org/abs/2204.05726
In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce a
Externí odkaz:
http://arxiv.org/abs/2203.10033
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL algorithms still require thousands of trials to converge to high-perfo
Externí odkaz:
http://arxiv.org/abs/2109.13050
Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Divers
Externí odkaz:
http://arxiv.org/abs/2012.04322
Les robots opèrent dans le monde réel, dans lequel essayer quelque chose prend beaucoup de temps. Pourtant, les methodes d’apprentissage par renforcement actuels (par exemple, deep reinforcement learning) nécessitent de longues périodes d
Externí odkaz:
http://www.theses.fr/2018LORR0276/document