1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
ConceptACT: Episode-Level Concepts for Sample-Efficient Robotic Imitation Learning
Jakob Karalus, Friedhelm Schwenker
Imitation learning enables robots to acquire complex manipulation skills from human demonstrations, but current methods rely solely on low-level sensorimotor data while ignoring th…
cs.AI2024
Tell me why: Training preferences-based RL with human preferences and step-level explanations
Jakob Karalus
Human-in-the-loop reinforcement learning allows the training of agents through various interfaces, even for non-expert humans. Recently, preference-based methods (PbRL), where the…
cs.AI2021★ 1 cited
Accelerating the Learning of TAMER with Counterfactual Explanations
Jakob Karalus, Felix Lindner
The capability to interactively learn from human feedback would enable agents in new settings. For example, even novice users could train service robots in new tasks naturally and…