Robots Need More than VLA and World Models
This position paper argues that generalist robot intelligence requires more than scaling VLA models; it needs mechanisms to convert unstructured behavioral data into robot supervision. The authors propose four missing interfaces: data, embodiment, world-model, and reward, and advocate for learning from the broader physical world.
[2606.06556] Robots Need More than VLA and World Models
[Submitted on 4 Jun 2026]
Title:Robots Need More than VLA and World Models
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Abstract: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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.06556 [cs.RO]
(or arXiv:2606.06556v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.06556
arXiv-issued DOI via DataCite
Submission history
From: Haitham Bou Ammar PhD [view email] [v1] Thu, 4 Jun 2026 10:43:14 UTC (549 KB)
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