Invited Speakers
NVIDIA
Stanford University
Johns Hopkins University
Stanford University
Scaling Laws and Diversity in Human-to-Robot
About the Workshop
This workshop continues the Human-to-Robot (H2R) line of inquiry on learning robot skills from human data, but deliberately narrows the question. Where prior efforts established whether robots can be taught from sensorized, modeled human behavior, the central premise here is that the binding constraint has now shifted from feasibility to scale and diversity: human data is a source of embodied experience that is abundant and cheap to collect, yet we still lack an empirical understanding of how robot capability grows as a function of how much human data we gather, how diverse it is, and how far the human embodiment sits from the target robot.
We bring together researchers in imitation learning, egocentric perception, dexterous manipulation, hardware co-design, and world modeling to ask what it actually takes to turn large, heterogeneous human data into reliable robot policies.
Core Challenges
Does robot policy performance improve predictably with the volume of human data, and along which axes — hours, tasks, demonstrators, scenes, embodiments — do returns actually accumulate versus saturate?
Which forms of diversity (task, scene, object, viewpoint, demonstrator morphology) drive downstream generalization, and how do we measure and compose for diversity rather than merely inflate dataset size?
How do morphological, kinematic, and contact/force mismatches degrade transfer as data scales, and can hardware co-design converge the two embodiments to make scaling effective?
Can action-conditioned, predictive world models learned from large-scale egocentric human video yield representations or simulators that transfer to robot control?
What pipelines for collection, retargeting, automatic labeling, and quality filtering scale to in-the-wild human data — and how do we benchmark whether added human data genuinely improves robot policies?
Program
Papers
The top contributed paper will receive the Best Paper Award, sponsored by NVIDIA.
Organizers
Sponsors
