1 citations · 2 across the 5 of their papers we have counts for
5 papers
LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning
Jelle Luijkx, Runyu Ma, Zlatan Ajanović +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…
ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning
Jelle Luijkx, Zlatan Ajanović, 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…
EAGERx: Graph-Based Framework for Sim2real Robot Learning
Bas van der Heijden, Jelle Luijkx, Laura Ferranti +2
Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics due to its potential to efficiently handle com…
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…
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning
Jelle Luijkx, Zlatan Ajanovic, Laura Ferranti +1
Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses).…