3 papers
cs.RO2026
Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning
Yuanzhi He, Victor Romero-Cano, José J. Patiño +3
As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorit…
cs.AI2025
Extending NGU to Multi-Agent RL: A Preliminary Study
Juan Hernandez, Diego Fernández, Manuel Cifuentes +2
The Never Give Up (NGU) algorithm has proven effective in reinforcement learning tasks with sparse rewards by combining episodic novelty and intrinsic motivation. In this work, we…
cs.RO2023
Learning to bag with a simulation-free reinforcement learning framework for robots
Francisco Munguia-Galeano, Jihong Zhu, Juan David Hernández +1
Bagging is an essential skill that humans perform in their daily activities. However, deformable objects, such as bags, are complex for robots to manipulate. This paper presents an…