4 citations
- Chart Industries (United States)US1 paper
- Cranfield UniversityGB1 paper
- École Nationale Supérieure de l'Électronique et de ses ApplicationsFR1 paper
- Equipes Traitement de l'Information et SystèmesFR1 paper
- Imperial College LondonGB1 paper
- Laboratoire Cognitions Humaine et ArtificielleFR1 paper
- Laboratoire Lorrain de Recherche en Informatique et ses ApplicationsFR1 paper
3 papers
cs.RO2026
Generalizing deep reinforcement learning across cable-driven parallel robot configurations with actuator-level policies
Abir Bouaouda, Mohamed Boutayeb, François Charpillet +2
Cable-driven parallel robots (CDPRs) present diverse configurations and complex control challenges, which can be addressed by deep reinforcement learning (DRL) by learning their no…
cs.RO2025
What Can Robots Teach Us About Trust and Reliance? An interdisciplinary dialogue between Social Sciences and Social Robotics
Julien Wacquez, Elisabetta Zibetti, Joffrey Becker +5
As robots find their way into more and more aspects of everyday life, questions around trust are becoming increasingly important. What does it mean to trust a robot? And how should…
cs.RO2016★ 4 cited
Towards semi-episodic learning for robot damage recovery
Konstantinos Chatzilygeroudis, Antoine Cully, Jean-Baptiste Mouret
The recently introduced Intelligent Trial and Error algorithm (IT\&E) enables robots to creatively adapt to damage in a matter of minutes by combining an off-line evolutionary algo…