Lukás Hermann — accepted AI-conference papers

2022

CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

Oier Mees, Lukás Hermann, Erick Rosete-Beas, Wolfram Burgard

RA-L 2022

General-purpose robots coexisting with humans in their environment must learn to relate human language to their perceptions and actions to be useful in a range of daily tasks. Moreover, they need to acquire a diverse repertoire of general-purpose skills that allow composing long-horizon tasks by fol

Cited by 518 Source ↗code ↗Scholar ↗

2022

What Matters in Language Conditioned Robotic Imitation Learning Over Unstructured Data

Oier Mees, Lukás Hermann, Wolfram Burgard

RA-L 2022

A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end l

Cited by 197 Source ↗code ↗Scholar ↗