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arxiv: 2605.31476 · v1 · pith:45NG7GPQnew · submitted 2026-05-29 · 💻 cs.RO

IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving

Chenghao Zhang , Timin Li , Dongmei Li This is my paper

Pith reviewed 2026-06-28 22:21 UTC · model grok-4.3

classification 💻 cs.RO
keywords end-to-end autonomous drivingworld modelsinverse dynamicslatent BEV predictiontrajectory optimizationfuture reasoning
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The pith

Inverse dynamics applied to pairs of predicted latent future states converts world-model forecasts into optimized driving trajectories.

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

The paper presents IDOL as a framework that adds an inverse dynamics step to latent future prediction inside a bird's-eye-view world model for end-to-end driving. It first generates multiple future scene states, then runs an inverse dynamics model on adjacent pairs of those states to recover motion deltas that directly inform trajectory updates. These deltas are fed into the planner so that scene anticipation produces concrete changes to the vehicle's path rather than remaining descriptive. A final closed-loop refinement pass reuses the updated trajectory to improve consistency over longer horizons. The result is presented as a tighter link between what the world model expects and what the planner executes, with reported gains on the NAVSIM v1 and v2 benchmarks.

Core claim

By inserting an inverse dynamics model between a BEV world model's predicted latent states and the trajectory optimizer, IDOL extracts transition-aware motion features from future forecasts and uses them to refine the planned path; this turns passive scene anticipation into explicit planning signals and is shown to raise performance on the NAVSIM benchmarks compared with prior comparable methods.

What carries the argument

An inverse dynamics model that takes adjacent predicted latent BEV states as input and outputs planning-relevant motion deltas to guide trajectory optimization.

If this is right

  • Future scene predictions become direct inputs to trajectory updates instead of remaining separate from motion generation.
  • A lightweight closed-loop refinement module re-applies future-aware reasoning to the optimized trajectory for better long-horizon consistency.
  • World-model-based planning achieves state-of-the-art results among comparable methods on the NAVSIM v1 and NAVSIM v2 benchmarks.
  • The overall coupling between latent world modeling and executable planning is tightened through the inverse-dynamics bridge.

Where Pith is reading between the lines

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

  • The same inverse-dynamics decoding step could be tested on other latent world models outside driving to see whether it improves planning in different robotic settings.
  • If the decoded motion deltas prove stable across diverse environments, they might reduce the need for separate motion-prediction heads in future end-to-end stacks.
  • Extending the closed-loop refinement to multiple iterations might further reduce drift in very long-horizon forecasts, though the paper does not explore that regime.

Load-bearing premise

An inverse dynamics model applied to pairs of predicted latent future states will reliably decode transition-aware trajectory features whose use in optimization produces measurable planning improvements.

What would settle it

An ablation that removes the inverse dynamics decoding step, retrains or re-optimizes the planner on the same NAVSIM data, and checks whether the reported performance advantage disappears.

Figures

Figures reproduced from arXiv: 2605.31476 by Chenghao Zhang, Dongmei Li, Timin Li.

Figure 1
Figure 1. Figure 1: Comparison of three planning paradigms. (a) Traditional methods directly map sensor [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed IDOL framework. (a) The closed-loop refinement module rolls [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative visualization of inverse-dynamics-guided refinement on the NAVSIM navtest [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative visualization of inverse-dynamics refinement under three representative driving [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Representative failure cases on navtest. [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
read the original abstract

End-to-end autonomous driving has emerged as a compelling paradigm for learning planning directly from sensor observations, while recent world-model-based approaches further enrich this paradigm by enabling explicit reasoning about how the scene may evolve in the future. Yet future prediction alone does not guarantee better planning unless the predicted evolution can be converted into planning-relevant trajectory updates. Many current methods still forecast future scene states without explicitly decoding the motion implications hidden in state transitions. As a result, future reasoning often remains descriptively useful but only weakly coupled to executable motion generation. To address this limitation, we propose \mathbf{IDOL}, an inverse-dynamics-guided future prediction framework for world-model-based end-to-end planning in latent BEV space, where inverse dynamics serves as the key bridge between future prediction and trajectory optimization. IDOL first predicts multiple future latent scene states with a BEV world model, then applies an inverse dynamics model to adjacent latent futures to decode transition-aware trajectory features and recover planning-relevant motion deltas that explain how the latent world evolves over time. These inverse-dynamics-derived signals are used to optimize the planned trajectory, turning future forecasting from passive scene anticipation into actionable planning guidance. A lightweight closed-loop refinement module further improves long-horizon consistency by reusing the optimized trajectory for another round of future-aware reasoning. By introducing inverse dynamics into latent future reasoning, IDOL tightens the coupling between world modeling and planning. Extensive experiments on the NAVSIM v1 and NAVSIM v2 benchmarks show that IDOL achieves state-of-the-art performance among comparable methods.

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

0 major / 0 minor

Summary. The manuscript proposes IDOL, an inverse-dynamics-guided future prediction framework for world-model-based end-to-end autonomous driving in latent BEV space. The method first predicts multiple future latent scene states with a BEV world model, then applies an inverse dynamics model to adjacent latent futures to decode transition-aware trajectory features and recover planning-relevant motion deltas. These signals are used to optimize the planned trajectory, with an additional lightweight closed-loop refinement module for long-horizon consistency. The central claim is that this approach tightens the coupling between world modeling and planning, supported by state-of-the-art empirical results on the NAVSIM v1 and NAVSIM v2 benchmarks.

Significance. If the reported benchmark results hold under scrutiny, the explicit use of inverse dynamics to convert latent state transitions into actionable motion deltas for trajectory optimization would constitute a meaningful technical contribution to world-model-based planning. The framework directly targets the noted gap between descriptive future prediction and executable planning updates, and the empirical validation on established NAVSIM benchmarks provides a concrete basis for assessing impact.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their review and for recognizing the potential contribution of using inverse dynamics to bridge future prediction and trajectory optimization in our IDOL framework. The report provides no specific major comments to address.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical ML framework for end-to-end driving that predicts latent future states via a BEV world model, applies an inverse-dynamics module to extract motion deltas, and uses those signals for trajectory optimization. No equations, parameter-fitting steps, or first-principles derivations are presented in the provided text that reduce a claimed prediction or result to its own inputs by construction. The central claims rest on benchmark performance (NAVSIM v1/v2) rather than any self-definitional loop, fitted-input-as-prediction, or self-citation chain. The inverse-dynamics component is introduced as an architectural choice whose value is demonstrated experimentally, not derived tautologically from the inputs it processes. This is a standard non-circular empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No concrete free parameters, axioms, or invented entities are identifiable from the abstract alone. The approach appears to build on standard components in world-model and inverse-dynamics literature without introducing new postulated entities.

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Reference graph

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