2 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…