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Source note2 min read

Latent Actions from Factorized Transition Effects under Agent Ambiguity

Source

arXiv

arxiv.org/abs/2606.30544v1

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Source

Plain-Language Brief

The study introduces Observed Transition Factorization (OTF) to help Latent Action Models (LAMs) understand actions in multi-object or distractor-rich scenes, where the source of action can be unclear due to mixed motion with distractors, camera dynamics, and background changes. OTF decomposes transitions into a set of observed transition primitives, which are then used as an interface for forming action-like latents in the standard inverse-forward dynamics framework. The study presents two models: OTF-LAM and OTF-LAM-Dino, with the latter being a decoder-free variant.

What the Document Says

  • Latent Action Models (LAMs) learn action-like proxies from observation transitions but struggle in multi-object or distractor-rich scenes due to ambiguous underlying action source.
  • The study proposes Observed Transition Factorization (OTF) which decomposes each transition into sparse observed transition primitives.
  • OTF primitives are used as the transition interface for two models: OTF-LAM and OTF-LAM-Dino.
  • OTF primitives transfer zeroshot across controlled carrier and morphology shifts, showing reusability.
  • Downstream policy learning results match or outperform baselines under complex transition ambiguity.

Key Concepts Mentioned

  • Latent Action Models (LAMs)
  • Observed Transition Factorization (OTF)
  • Transition primitives
  • Action-like latents
  • Inverse-forward dynamics framework
  • OTF-LAM
  • OTF-LAM-Dino

Claims Made by the Document

  • LAMs struggle in multi-object or distractor-rich scenes due to ambiguous underlying action source.
  • OTF primitives transfer zeroshot across controlled carrier and morphology shifts, showing reusability.
  • Downstream policy learning results match or outperform baselines under complex transition ambiguity.

Evidence Used

  • The study mentions that LAMs learn action-like proxies from observation transitions but struggle in complex scenes due to mixed motion with distractors, camera dynamics, and background changes.
  • The text explains that OTF primitives transfer zeroshot across controlled carrier and morphology shifts as shown empirically.
  • The authors state that downstream policy learning results match or outperform baselines under complex transition ambiguity.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Latent Action Models (LAMs) learn action-like proxies from observation transitions. However, in multi-object or distractor-rich scenes, these visual effects mix agent motion with distractors, camera dynamics, and background changes, making the underlying action source ambiguous without supervision. Structuring this mixture as reusable transition effects provides an intermediate representation from which action-like latents can be more robustly formed. We introduce Observed Transition Factorization (OTF), which decomposes each transition into a sparse set of observed transition primitives. Using these primitives as the transition interface, we propose OTF-LAM, which abstracts motion primitives into action-like latents within the standard inverse-forward dynamics framework, and OTF-LAM-Dino, a decoder-free variant that predicts future states in a frozen DINOv2 representation space. Empirically, OTF primitives transfer zeroshot across controlled carrier and morphology shifts, showing reusability. Furthermore, downstream policy learning results match or outperform baselines under complex transition ambiguity.