DF$^3$: World Modeling via Decoder-Free Feature Forecasting in Autonomous Navigation

• URL: http://arxiv.org/abs/2608.02428v1
• Date: Mon, 03 Aug 2026 16:08:59 GMT
• Title: DF$^3$: World Modeling via Decoder-Free Feature Forecasting in Autonomous Navigation
• Abstract summary: DF$3$ injects learnable spatial queries into the terminal blocks of a frozen vision foundation model to extract future state representations directly.<n>Experiments on public benchmarks and zero-shot deployment in a robotic simulator demonstrate that DF$3$ achieves performance comparable to state-of-the-art methods.
• Score: 7.872843554385665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasting future states from video sequences is a critical challenge for autonomous robotic systems and a fundamental objective of world modeling. Prior generative methods operating at the pixel level inevitably overemphasize task-irrelevant details, leading to prohibitive computational overhead. While latent-based approaches attempt to mitigate this by predicting features directly, the persistent reliance on heavy decoders for state-to-task mapping remains a computational bottleneck. In this work, we propose Decoder-Free Feature Forecasting (DF$^3$), a novel framework that models world evolution entirely within the latent space and directly derives task outputs, completely eliminating the need for a decoder. Specifically, DF$^3$ injects learnable spatial queries into the terminal blocks of a frozen vision foundation model to extract future state representations directly. By employing a lightweight, unified Motion-Aware Context Fusion (MACF) mechanism that seamlessly integrates coarse flow warping with fine-grained latent cross-correlation, these queries interact with historical token representations to explicitly align and forecast the feature of the next frame. Subsequently, a specialized set of task queries probes these forecasted features for the downstream task. Extensive experiments on public benchmarks and zero-shot deployment in a robotic simulator demonstrate that DF$^3$ achieves performance comparable to state-of-the-art methods while offering superior efficiency and flexibility for integrated perception and control.

Related papers

• StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models [84.17128030384099]
  StreamPI is a streaming multimodal temporal modeling framework.<n>It equips single-frame VLA with temporal reasoning capability without introducing any additional parameters.<n> Experiments on real-robot tasks spanning memory-dependent and precise perception scenarios, as well as the simulation benchmark LIBERO, demonstrate that StreamPI outperforms pi0.5 across diverse tasks.
  arXiv  Detail & Related papers  (2026-08-26T17:33:19Z)
• KineBench: Benchmarking Embodied World Models via IDM-Free Kinematic Grounding [58.541098216700796]
  evaluating the physical consistency of embodied world models is a critical open challenge.<n>Existing frameworks almost exclusively rely on Inverse Dynamics Models(IDMs) for action extraction.<n>We present KineBench, an IDM-free closed-loop benchmark for EWMs.
  arXiv  Detail & Related papers  (2026-07-22T08:04:17Z)
• Flow Matching in Feature Space for Stochastic World Modeling [21.159664319255665]
  We propose FlowWM, a world model that performs flow matching directly within pretrained feature space.<n>This is challenging because pretrained features are substantially high-dimensional, making standard diffusion recipes suboptimal.<n>We evaluate FlowWM on two benchmarks: a synthetic benchmark for systematic evaluation of accuracy and diversity, and a real-world benchmark FuturePerception.
  arXiv  Detail & Related papers  (2026-06-27T19:35:27Z)
• PRISM: PRior-guided Imagination Sampling in world Models [7.304186330343712]
  We introduce PRISM, a task-agnostic framework that extracts both from a single dataset.<n>PRISM improves success rates by 35 percentage points over vanilla world-model-based MPC on Cube and 32 percentage points on PushT.
  arXiv  Detail & Related papers  (2026-06-06T04:18:10Z)
• OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models [69.2503510410147]
  We present a unified autonomous driving framework built upon a pretrained VLM.<n>We demonstrate that pretrained VLM attention exhibits strong transferability beyond pure language modeling.<n>Experiments on end-to-end autonomous driving benchmarks demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-20T07:50:00Z)
• DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks [54.32016216994156]
  We introduce the Causal Latent World Model (CLWM), which employs DINOv3 features as generative targets to disentangle interaction semantics from visual noise.<n>To overcome memory scaling, CLWM features a Dual-State Test-Time Training (TTT) Memory that guarantees a strict $mathcalO(1)$ footprint for long-horizon tasks.<n>We present EmbodiChain, an online framework that establishes the Efficiency Law by injecting an infinite flow of physics-grounded trajectories during training.
  arXiv  Detail & Related papers  (2026-04-13T03:19:36Z)
• V-CAGE: Vision-Closed-Loop Agentic Generation Engine for Robotic Manipulation [6.820118518027692]
  We present V-CAGE, an agentic framework for autonomous robotic data synthesis.<n>Unlike traditional scripted pipelines, V-CAGE operates as an embodied agentic system.<n>To overcome the storage bottleneck of massive video datasets, we implement a perceptually-driven compression algorithm.
  arXiv  Detail & Related papers  (2026-04-10T06:56:17Z)
• RADAR: Closed-Loop Robotic Data Generation via Semantic Planning and Autonomous Causal Environment Reset [48.645870795753105]
  We introduce Robust Autonomous Data Acquisition for Robotics (RADAR)<n>RADAR is a fully autonomous, closed-loop data generation engine that completely removes human intervention from the collection cycle.<n>In simulation, our framework achieves up to 90% success rates on complex, long-horizon tasks.
  arXiv  Detail & Related papers  (2026-03-12T11:18:52Z)
• Future Optical Flow Prediction Improves Robot Control & Video Generation [100.87884718953099]
  We introduce FOFPred, a novel optical flow forecasting model featuring a unified Vision-Language Model (VLM) and Diffusion architecture.<n>Our model is trained on web-scale human activity data-a highly scalable but unstructured source.<n> Evaluations across robotic manipulation and video generation under language-driven settings establish the cross-domain versatility of FOFPred.
  arXiv  Detail & Related papers  (2026-01-15T18:49:48Z)
• Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method [54.461213497603154]
  Occupancy-centric methods have recently achieved state-of-the-art results by offering consistent conditioning across frames and modalities.<n>Nuplan-Occ is the largest occupancy dataset to date, constructed from the widely used Nuplan benchmark.<n>We develop a unified framework that jointly synthesizes high-quality occupancy, multi-view videos, and LiDAR point clouds.
  arXiv  Detail & Related papers  (2025-10-27T03:52:45Z)
• STAMP: Scalable Task And Model-agnostic Collaborative Perception [24.890993164334766]
  STAMP is a task- and model-agnostic, collaborative perception pipeline for heterogeneous agents.<n>It minimizes computational overhead, enhances scalability, and preserves model security.<n>As a first-of-its-kind framework, STAMP aims to advance research in scalable and secure mobility systems towards Level 5 autonomy.
  arXiv  Detail & Related papers  (2025-01-24T16:27:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.