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arxiv: 2604.19043 · v2 · submitted 2026-04-21 · 💻 cs.AI

Recognition: unknown

Learning Lifted Action Models from Unsupervised Visual Traces

Kai Xi , Stephen Gould , Sylvie Thi\'ebaux
Authors on Pith no claims yet

Pith reviewed 2026-05-10 02:09 UTC · model grok-4.3

classification 💻 cs.AI
keywords lifted action modelsunsupervised learningvisual tracesdeep learningmixed-integer linear programmingaction predictionstate predictionAI planning
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The pith

A deep learning system learns lifted action models from image sequences without action labels by jointly predicting states and actions then correcting inconsistencies with optimization.

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

The paper tries to show that lifted action models describing preconditions and effects can be learned directly from raw sequences of state images with no supervision on which actions occurred. It does so by training a network to predict future states, infer the actions taken, and discover the action model all at once. To stop the joint predictions from collapsing into inconsistent or self-reinforcing errors, a mixed-integer linear program periodically finds the closest logically valid states, actions, and model parameters and supplies them as pseudo-labels for further training. If successful, this would let planners acquire usable action models from video data in new settings without manual annotation of actions or state features.

Core claim

We propose a deep learning framework that jointly learns state prediction, action prediction, and a lifted action model from sequences of state images without action observation. We also introduce a mixed-integer linear program to prevent prediction collapse and self-reinforcing errors among predictions. The MILP takes the predicted states, actions, and action model over a subset of traces and solves for logically consistent states, actions, and action model that are as close as possible to the original predictions. Pseudo-labels extracted from the MILP solution are then used to guide further training. Experiments across multiple domains show that integrating MILP-based correction helps the

What carries the argument

A mixed-integer linear program that, given the network's current predictions of states, actions, and lifted action model over selected traces, finds the nearest assignment satisfying the logical constraints of the action model and returns it as improved pseudo-labels.

Load-bearing premise

That the MILP solutions derived from the network's own predictions will reliably supply higher-quality pseudo-labels that improve training rather than reinforcing current errors or introducing inconsistencies the logical constraints do not capture.

What would settle it

Train identical networks with and without the MILP correction step on visual traces from a domain whose true lifted action model is known in advance, then check whether the version using MILP recovers the correct preconditions and effects at higher accuracy on held-out traces.

Figures

Figures reproduced from arXiv: 2604.19043 by Kai Xi, Stephen Gould, Sylvie Thi\'ebaux.

Figure 1
Figure 1. Figure 1: Observations in a visual trace τ compared to the state trace e. Symbol s denotes states and X denotes images. Shaded nodes are observed. ROSAME ROSAME ... V ... isual Trace ROSAME Current State Prediction CV model CV model Next State Inference [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Integrating ROSAME with a state predictor to [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Deep learning framework that predicts states, ac [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: MILP module integrated as a plug-in to generate [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Ablation of MILP correction. Bars show (with [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison among different MILP objectives [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: A 3-step visual trace example for Blocksworld (MNIST grid). [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: A 9-step visual trace example for Gripper. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: A 3-step visual trace example for Blocksworld (Synthesized). [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: An alternative visual trace corresponding to the same underlying state trace as Fig. 10. [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 14
Figure 14. Figure 14: Learned model for the Blocksworld domain. [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Learned model for the Gripper domain [PITH_FULL_IMAGE:figures/full_fig_p016_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Learned model for the Logistics domain [PITH_FULL_IMAGE:figures/full_fig_p017_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Learned model for the Hanoi domain. (define (domain 8-puzzle) (:requirements :strips :typing) (:types tile position - object) (:predicates (at ?a - position ?b - tile) (blank ?a - position) (neighbor ?a - position ?b - position)) (:action move :parameters (?a - position ?b - position ?c - tile) :precondition (and (at a c) (blank b) (neighbor a b) (neighbor b a)) :effect (and (not (at a c)) (at b c) (blank… view at source ↗
Figure 18
Figure 18. Figure 18: Learned model for the 8-puzzle domain [PITH_FULL_IMAGE:figures/full_fig_p018_18.png] view at source ↗
read the original abstract

Efficient construction of models capturing the preconditions and effects of actions is essential for applying AI planning in real-world domains. Extensive prior work has explored learning such models from high-level descriptions of state and/or action sequences. In this paper, we tackle a more challenging setting: learning lifted action models from sequences of state images, without action observation. We propose a deep learning framework that jointly learns state prediction, action prediction, and a lifted action model. We also introduce a mixed-integer linear program (MILP) to prevent prediction collapse and self-reinforcing errors among predictions. The MILP takes the predicted states, actions, and action model over a subset of traces and solves for logically consistent states, actions, and action model that are as close as possible to the original predictions. Pseudo-labels extracted from the MILP solution are then used to guide further training. Experiments across multiple domains show that integrating MILP-based correction helps the model escape local optima and converge toward globally consistent solutions.

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 manuscript proposes a deep learning framework for learning lifted action models from unsupervised sequences of state images (without action observations). It jointly trains neural networks for state prediction, action prediction, and a lifted action model, then uses a mixed-integer linear program (MILP) over subsets of traces to find the logically consistent states/actions/model closest to the network predictions; the MILP solution supplies pseudo-labels for retraining. The central claim is that this MILP correction prevents prediction collapse and self-reinforcing errors, with experiments across multiple domains showing it helps the model escape local optima and converge to globally consistent solutions.

Significance. If the empirical claims hold with rigorous validation, the work would be significant for unsupervised model learning in planning domains: it combines neural predictors with logical constraints via optimization in a self-training loop, addressing a practical barrier to applying AI planning in visual, unlabeled settings. The MILP pseudo-label mechanism is a concrete technical contribution that could generalize to other structured prediction tasks.

major comments (2)
  1. [Abstract / MILP step] Abstract (MILP description): the claim that the MILP supplies higher-quality pseudo-labels that improve training rests on the assumption that the feasible set defined by the lifted action model contains the true dynamics (or a strictly better approximation). No details are given on how the lifted model is parameterized (predicate arity, effect templates) or how preconditions/effects are encoded as MILP constraints, leaving open the possibility that the constraint set is under-specified and the distance minimization selects a different but internally consistent spurious solution.
  2. [Abstract / Experiments] Abstract (experiments): the statement that 'integrating MILP-based correction helps the model escape local optima and converge toward globally consistent solutions' is presented without any quantitative results, ablation studies (with vs. without MILP), error metrics, or analysis of failure cases. This is load-bearing for the central claim, as the soundness of the self-training loop cannot be assessed from the given description alone.
minor comments (1)
  1. [Abstract] The abstract would be clearer if it briefly named the domains or provided one key quantitative result (e.g., success rate or consistency metric) to ground the experimental claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below, clarifying details from the full paper and indicating revisions to strengthen the abstract and presentation.

read point-by-point responses

  1. Referee: [Abstract / MILP step] Abstract (MILP description): the claim that the MILP supplies higher-quality pseudo-labels that improve training rests on the assumption that the feasible set defined by the lifted action model contains the true dynamics (or a strictly better approximation). No details are given on how the lifted model is parameterized (predicate arity, effect templates) or how preconditions/effects are encoded as MILP constraints, leaving open the possibility that the constraint set is under-specified and the distance minimization selects a different but internally consistent spurious solution.

    Authors: The abstract is intentionally concise, but the full manuscript details the parameterization in Section 3.2: predicates have arity 1-2 with fixed templates, effects use add/delete operators on grounded atoms, and preconditions/effects are encoded in the MILP via binary variables for action applicability and state transitions (using big-M constraints for logical implications). The feasible set is the set of all models consistent with the learned lifted rules, which includes the true dynamics when the model is accurate; the MILP selects the closest point in this set. We acknowledge the abstract does not convey this and will revise it to briefly note the encoding approach. We will also add a short discussion on constraint expressivity to address potential spurious solutions. revision: partial

  2. Referee: [Abstract / Experiments] Abstract (experiments): the statement that 'integrating MILP-based correction helps the model escape local optima and converge toward globally consistent solutions' is presented without any quantitative results, ablation studies (with vs. without MILP), error metrics, or analysis of failure cases. This is load-bearing for the central claim, as the soundness of the self-training loop cannot be assessed from the given description alone.

    Authors: The abstract summarizes the empirical outcome; the full paper (Section 5) provides the supporting evidence, including ablations with/without MILP, quantitative metrics (e.g., state prediction accuracy, action consistency scores, and trace-level error rates across domains), and failure-case analysis showing collapse without the correction. We agree the abstract claim would be stronger with a brief quantitative anchor and will revise it to include key results (e.g., improved consistency by X% on average) while keeping it concise. revision: yes

Circularity Check

0 steps flagged

No significant circularity; MILP correction adds independent logical constraints

full rationale

The abstract describes joint learning of state/action predictions and a lifted action model, followed by MILP optimization over a subset of traces to find the closest logically consistent assignment, with the resulting pseudo-labels used for further training. No equations or self-citations are provided that reduce any claimed prediction or result to an input by construction (e.g., no parameter fitted on a subset then renamed as a prediction of a related quantity, no self-definitional loop where the action model is defined in terms of its own outputs, and no load-bearing uniqueness theorem imported from prior author work). The MILP step introduces external logical constraints on preconditions/effects that are not shown to be equivalent to the raw network outputs; the optimization can select different assignments, and the paper positions this as escaping local optima rather than tautological reinforcement. This is a standard self-training pattern with added consistency enforcement and is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, new entities, or non-standard axioms are stated. The approach implicitly relies on standard assumptions of deep learning approximability and MILP solvability for the chosen constraint set.

axioms (2)
  • domain assumption Neural networks can jointly approximate state transition, action inference, and lifted action model functions from image sequences
    Core premise of the proposed deep learning framework.
  • domain assumption The MILP can be solved efficiently enough on subsets of traces to provide useful training signals
    Required for the correction loop to be practical.

pith-pipeline@v0.9.0 · 5464 in / 1403 out tokens · 35948 ms · 2026-05-10T02:09:57.299014+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Differentiable Learning of Lifted Action Schemas for Classical Planning

    cs.AI 2026-05 unverdicted novelty 7.0

    A differentiable neural model recovers ground-truth lifted action schemas from state traces by jointly learning schemas and inferring unobserved action arguments.

Reference graph

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