Publikation
Robots Need More than VLA and World Models
Elis Karcini; Faisal Mehrban; Quang Nguyen; Mac Schwager; Arash Ajoudani; Cesar Cadena; Jan Peters; Marco Hutter; Haitham Bou-Ammar
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.06556, Pages 1-26, arXiv, 2026.
Zusammenfassung
Generalist robot intelligence is often framed as a policy-scaling problem: collect
more robot demonstrations, train larger Vision-Language-Action (VLA) models,
and expect broader generalisation. In this position paper, we argue that this framing
is incomplete. The central bottleneck is not only policy learning, but the absence of
mechanisms that convert the world’s abundant unstructured behavioural data into
grounded robot supervision. Human motion, internet video, simulation rollouts, and
interactive demonstrations contain rich information about tasks, goals, contacts,
failures, and physical constraints, yet most of this information is not directly
usable by robot policies because it lacks embodiment-specific action labels, task
semantics, and reward structure. We identify four missing components for the next
generation of robotics: data interfaces for autolabelling unstructured behaviour,
embodiment interfaces for retargeting human motion to robot actions, world-model
interfaces for physics-grounded 3D reasoning, and reward interfaces for inferring
task progress and success from video and language. We survey recent progress in
robot foundation models, cross-embodiment datasets, learning from video, world
models, and reward modelling, and propose a research agenda for building robotics
systems that can learn not only from robot demonstrations, but from the broader
physical world.