2026-06-04原文2 min readUpdated: 2026-06-07

CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization

CLAW is a fully end-to-end self-supervised framework that learns world models and continuous latent action representations from action-free videos using adversarial latent regularization and diffusion-based generation. It enables imitation learning from observation and goal-directed planning, outperforming existing methods.

SourcearXiv RoboticsAuthor: Tewodros Ayalew, Matthew Jeung, Samuel Wheeler, Xiao Zhang, Andre de la Cruz Arce, Kaylene Stocking, Michael Maire, Matthew R. Walter

[2606.04130] CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization

[Submitted on 2 Jun 2026]

Title:CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization

View a PDF of the paper titled CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization, by Tewodros Ayalew and 7 other authors

View PDF HTML (experimental)

Abstract:We introduce CLAW, a fully end-to-end self-supervised framework for learning a world model jointly with continuous latent action representations directly from action-free videos. Our approach leverages adversarial latent regularization and diffusion-based video generation to capture structured and semantically meaningful action representations while modeling rich, predictive environment dynamics, without relying on any action labels or annotations. By simultaneously training the Latent Action Model and world model, CLAW learns to reason about how inferred actions induce environment transitions from visual observations alone. We show that the resulting latent action world model supports both imitation learning from observation and goal-directed planning. In imitation learning, latent actions extracted from raw videos enable behavior cloning. For planning, CLAW generates sequences of latent actions and maps them to executable actions to reach desired goals. Extensive experiments across diverse tasks and embodiments demonstrate that CLAW produces semantically meaningful latent action representations, supports effective action transfer, and enables planning and imitation from observation, outperforming existing methods.

Comments: 8 pages, 15 pages of supplementary material

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2606.04130 [cs.RO]

(or arXiv:2606.04130v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.04130

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tewodros Ayalew [view email] [v1] Tue, 2 Jun 2026 18:40:24 UTC (37,235 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization, by Tewodros Ayalew and 7 other authors

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.RO

new | recent | 2026-06

Change to browse by:

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)