3 papers
cs.RO2026
LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning
Jelle Luijkx, Runyu Ma, Zlatan AjanoviÄ +1
Reinforcement learning (RL) is a promising approach for robotic manipulation, but it can suffer from low sample efficiency and requires extensive exploration of large state-action…
cs.LG2025
ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning
Jelle Luijkx, Zlatan AjanoviÄ, Laura Ferranti +1
Human teaching effort is a significant bottleneck for the broader applicability of interactive imitation learning. To reduce the number of required queries, existing methods employ…
cs.RO2025
ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Runyu Ma, Jelle Luijkx, Zlatan Ajanovic +1
In robot manipulation, Reinforcement Learning (RL) often suffers from low sample efficiency and uncertain convergence, especially in large observation and action spaces. Foundation…