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arxiv: 2606.06832 · v1 · pith:WQJJ7UWBnew · submitted 2026-06-05 · 💻 cs.RO

STRIPS-WM: Learning Grounded Propositional STRIPS-style World Models from Images

Abhiroop Ajith , Constantinos Chamzas This is my paper

Pith reviewed 2026-06-27 22:09 UTC · model grok-4.3

classification 💻 cs.RO
keywords STRIPS world modelsvisual task planninggrounded symbolic learningrobot manipulationpropositional operatorsimage-to-plan
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The pith

STRIPS-WM learns latent binary predicates and propositional operators from image transitions to support classical planning from new start and goal images.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a framework that turns raw sequences of robot images into a symbolic planning model. It first extracts a finite abstract transition graph from observed image transitions, then identifies latent binary predicates and one grounded operator per action that encode preconditions and add/delete effects. These elements are distilled into a visual encoder so that a classical planner can produce action sequences for previously unseen images. Experiments on visual rearrangement tasks show higher image-to-plan success rates than visual rollout, latent graph-search, and latent-symbolic baselines. The approach aims to combine the structure of classical planning with learning directly from visual data.

Core claim

STRIPS-WM induces a finite abstract transition graph from images, learns latent binary predicates and one grounded propositional operator per action label that form a symbolic action model with sparse preconditions and add/delete effects, then distills the predicates into a visual encoder that enables classical planning directly from novel start and goal images.

What carries the argument

The grounded propositional STRIPS-style world model consisting of latent binary predicates and learned operators extracted from visual transitions.

If this is right

  • Classical planners operate directly on image inputs without requiring visual rollouts or latent-space search.
  • The learned operators produce sparse, interpretable preconditions and effects for each action label.
  • Planning performance improves over tested visual, latent-graph, and latent-symbolic methods on rearrangement tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The predicates might transfer to new scenes if the visual encoder generalizes beyond the training distribution.
  • The method could reduce reliance on manually designed symbolic models when deploying planners in visual robotics settings.
  • Similar induction of abstract graphs and predicates might apply to other high-dimensional observation spaces such as point clouds.

Load-bearing premise

A finite abstract transition graph can be reliably induced from raw image transitions such that the resulting latent binary predicates capture action applicability and effects well enough for classical planning on unseen images.

What would settle it

Running the learned planner on held-out image pairs from the rearrangement tasks and finding that success rates fall below those of the visual rollout baseline would show the induced model does not support the claimed planning improvement.

Figures

Figures reproduced from arXiv: 2606.06832 by Abhiroop Ajith, Constantinos Chamzas.

Figure 1
Figure 1. Figure 1: Three planning spaces for the same visual task. Top: In image space, the robot observes RGB states connected by high-level action identifiers, ot at −→ ot+1; planning here would require reasoning over future images. Middle: In the learned task-graph space, observations are compressed into abstract states sˆ, yielding action-indexed transitions sˆt at −→ sˆt+1. This enables graph search, but requires a flat… view at source ↗
Figure 2
Figure 2. Figure 2: Learning the image-grounded task graph: A student encoder maps the current observation to a quantized FSQ code, while an EMA teacher encoder provides the target successor code. An action-conditioned dynamics model predicts the next code from the current code and action identifier, and an inverse model predicts the action from current and successor codes. Unique quantized codes define visual task-graph node… view at source ↗
Figure 3
Figure 3. Figure 3: Planning success as a function of shortest-path horizon. Each point is evaluated by executing the returned action sequence in the ground-truth transition graph. Success therefore requires reaching the true goal state, not merely the goal predicted by the learned model. At test time, the input is a start image ostart and a goal image ogoal. The visual predicate classifier maps them to predicate probabilitie… view at source ↗
Figure 4
Figure 4. Figure 4: Planning success on DinnerTable Real. RQ3: Are predicates better than monolithic graph search? We compare STRI PS-W M to direct graph search over the learned task graph Gˆ = (Sˆ, Eˆ). As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Negative-evidence ablation on DinnerTable and DinnerTable Real. Removing negative evidence makes planning fail, even when the solver is guided with the true minimum number of task states. Domain Condition |S ⋆ | Pξ Pη False positives |Xˆ| DinnerTable STRIPS-WM 101 0 9 9 101 DinnerTable No neg. 101 0 0 6864 2 DinnerTable No neg. + min-unique 101 0 0 6864 101 DinnerTable Real STRIPS-WM 71 0 13 13 71 DinnerTa… view at source ↗
read the original abstract

Robots performing long-horizon visual manipulation observe high-dimensional images, but successful plans depend on action-relevant facts: what can be done now and what changes afterward. A useful planning representation should discard irrelevant visual details while preserving action applicability and effects. Classical task planners exploit this structure through symbolic operators with preconditions and effects, but obtaining such representations from raw visual experience remains challenging. We study a visual task-planning setting in which a robot receives only image transitions: the current image, executed high-level action, and the resulting image. At test time, given a start image and a goal image, the robot must produce a sequence of high-level actions that reaches the goal. To address this problem, we introduce STRIPS-WM, a framework for learning image-grounded STRIPS-style world models directly from visual transitions. STRIPS-WM first induces a finite abstract transition graph from images, then learns latent binary predicates and one grounded propositional operator per action label. The learned operators form a symbolic action model with sparse preconditions and add/delete effects. Finally, the learned predicates are distilled into a visual encoder, enabling classical planning directly from novel start and goal images. Experiments on visual rearrangement tasks show that STRIPS-WM improves image-to-plan success over the tested visual rollout, latent graph-search and latent-symbolic baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces STRIPS-WM, a framework for learning image-grounded STRIPS-style world models from visual transitions. It first induces a finite abstract transition graph from images, then learns latent binary predicates and one grounded propositional operator per action label with sparse preconditions and add/delete effects. These predicates are distilled into a visual encoder to enable classical planning directly from novel start and goal images. Experiments on visual rearrangement tasks are claimed to show improved image-to-plan success over visual rollout, latent graph-search, and latent-symbolic baselines.

Significance. If the central empirical claim holds, the work would demonstrate a viable pipeline for inducing symbolic planning operators directly from raw image transitions, bridging high-dimensional visual input with classical task planning in robotics. The explicit grounding of predicates and operators, together with the use of an off-the-shelf planner at test time, would be a concrete contribution to visual task planning.

major comments (2)
  1. [Abstract, §3] Abstract and §3 (method overview): the induction procedure for the finite abstract transition graph is described only at a high level; no information is given on the clustering criterion, embedding function, or termination condition. Because the subsequent predicate-learning step relies on action applicability and effects being consistent within each abstract state, the absence of these details leaves open the possibility that state aliasing or combinatorial blow-up will invalidate the downstream classical planning step on unseen images.
  2. [Abstract] Abstract (experiments paragraph): the claim that STRIPS-WM improves image-to-plan success is stated without any quantitative results, baseline implementation details, dataset description, number of trials, or statistical tests. This information is load-bearing for the central empirical claim and cannot be assessed from the provided text.
minor comments (1)
  1. Notation for the latent predicates and operators is introduced without an explicit table or running example that shows how a concrete image transition maps to a precondition/effect tuple.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments point by point below.

read point-by-point responses

  1. Referee: [Abstract, §3] Abstract and §3 (method overview): the induction procedure for the finite abstract transition graph is described only at a high level; no information is given on the clustering criterion, embedding function, or termination condition. Because the subsequent predicate-learning step relies on action applicability and effects being consistent within each abstract state, the absence of these details leaves open the possibility that state aliasing or combinatorial blow-up will invalidate the downstream classical planning step on unseen images.

    Authors: We agree the abstract and high-level overview in §3 are concise. The full details of the induction procedure (including the embedding function, clustering criterion, and termination condition) appear in Section 3.1 of the manuscript. To improve accessibility we will expand the method overview paragraph to restate these elements explicitly and add a short discussion of how predicate learning enforces consistency, thereby addressing concerns about aliasing and blow-up. The empirical results in Section 5 provide evidence that the learned model supports successful planning on unseen images. revision: yes

  2. Referee: [Abstract] Abstract (experiments paragraph): the claim that STRIPS-WM improves image-to-plan success is stated without any quantitative results, baseline implementation details, dataset description, number of trials, or statistical tests. This information is load-bearing for the central empirical claim and cannot be assessed from the provided text.

    Authors: Abstracts conventionally summarize results at a qualitative level for brevity. All requested quantitative details, baseline implementations, dataset description, trial counts, and statistical tests are reported in full in Section 5. We do not believe it is necessary or conventional to embed these specifics in the abstract itself, but we are happy to add a single sentence with key success-rate numbers if the editor prefers. revision: no

Circularity Check

0 steps flagged

No circularity: data-driven induction and learning pipeline with independent empirical validation

full rationale

The paper presents STRIPS-WM as a sequence of learning steps (induce abstract transition graph from image transitions, learn latent predicates and grounded operators, distill to visual encoder) evaluated via image-to-plan success on rearrangement tasks against baselines. No equations, fitted parameters renamed as predictions, or self-citation chains are described that would reduce the claimed operators or planning performance to the inputs by construction. The approach is explicitly positioned as learning from raw visual experience, with success measured externally on held-out images, satisfying the criteria for a self-contained, non-circular derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, so no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.1-grok · 5766 in / 1044 out tokens · 19571 ms · 2026-06-27T22:09:23.232032+00:00 · methodology

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Reference graph

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    grounded propositionalS T R I P Soperators{ˆωa}a∈A over thesempredicates, such that:

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    every observed edge(ˆs, a,ˆs′)∈ ˆEsatisfies the preconditions ofˆωa at ˆxˆs

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    18 Proof

    every selected distinctness constraint is satisfied. 18 Proof. For the forward direction, suppose the formulation is feasible in the exact regime. By Proposi- tion 7, the induced operators are well-formed grounded propositionalS T R I P Soperators. By Lemma 4, every observed edge satisfies the learned preconditions. By Corollary 3, every observed edge is ...