7th Robot Learning Workshop: Towards Robots with Human-Level Abilities

Andrey Kolobov, Dhruv Shah, Anqi Li, Feras Dayoub, Roberto Calandra, Ted Xiao, Rika Antonova, Nur Muhammad Mahi Shafiullah, Masha Itkina

Keywords: robot learning, humanoid, foundation models, large models, decision-making

TL;DR: Robot hardware is getting physically capable of human-level performance; how do we make progress on ML methods to take advantage of these capabilities?

Abstract: The year 2024 has seen an explosion of interest in humanoid robots. In the 7th Robot Learning workshop, to be held at ICLR-2025, we will look beyond the humanoid embodiment and ask: how far are we from robots with human-level abilities? What do we need to improve about embodied learning, decision-making, perception, and data collection to train generally physically capable robots to robustly perform a wide range of activities such as cooking or tidying up the house -- activities that people do without much thinking? We believe many of the weaknesses of the current robotic systems to be a reflection of the shortcomings of general AI methods and models. As such, in this workshop we will seek diverse perspectives from robotics-focused and robotics-orthogonal parts of the ICLR community alike, scientific contributions from academia and industry, as well as participants from a variety of backgrounds and career stages. Capitalizing on our prior experience with robotics showcases, in keeping with the spirit of the times we will solicit several humanoid robotics companies to exhibit their robots during the workshop's poster sessions.

Submission Number: 113