Beyond Teleoperation

Introduction

Teleoperation has driven progress in robot learning, but it is fundamentally limiting: control interfaces introduce latency and reduce degrees of freedom, producing demonstrations that lack the natural dexterity and diverse strategies humans exhibit in everyday manipulation. Human video and simulation each offer qualitatively different strengths. Unscripted human video captures whole-body coordination, tool use, and manipulation strategies that no teleoperation interface can reproduce. Simulation enables exploration of contact-rich dynamics, failure recovery, and edge cases at scale. Yet both carry challenges: embodiment mismatch and the sim-to-real gap. The central question is not just how do we get more data, but how do we extract what makes these sources qualitatively richer, and bridge the gaps that separate them from deployable robot skills?

This workshop focuses on methods that:

• Learn dexterous manipulation skills from human demonstrations, going beyond what teleoperation interfaces can capture
• Build world models grounded in real-world physics from diverse, in-the-wild video data
• Use small, high-quality teleoperation data as an anchor alongside large-scale, diverse off-domain sources
• Bridge embodiment and sim-to-real gaps to transfer manipulation skills across domains

We bring together researchers working on learning from human data, simulation, and robot teleoperation to share insights and collaborate on building more general-purpose manipulation systems.