Alexander Herzog

TL;DR: It is shown how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.

TL;DR: In this article , the authors present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties and verify their conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size and data diversity based on a large-scale data collection on real robots performing real-world tasks.

TL;DR: In this article , the authors describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings.

TL;DR: A first formalization and experimental validation of incremental action-repair for robotic instruction-following based on reinforcement learning is presented and it is shown that a reinforcement learning agent can successfully learn to understand incremental corrections of misunderstood instructions.