DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation

DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation

Abstract

Aerial vision-language navigation (VLN) requires an embodied agent to integrate visual evidence over time, plan future actions, and determine when it has reached a navigation goal under partial observability. Although recent VLA models offer a promising perception-to-action paradigm, adapting them to aerial navigation remains challenging. Policies conditioned primarily on the current observation may lose track of previously observed landmarks; single-step action prediction provides limited lookahead; and implicit termination through action generation may be unreliable.

To address these challenges, we propose DreamFly, a diffusion-based aerial VLN framework built on Dream-VLA. DreamFly first introduces a causally aligned historical memory that augments the current visual representation with evidence drawn exclusively from observations preceding the current decision step, thereby enabling temporal reasoning without leaking future information. Leveraging the bidirectional diffusion backbone, we further formulate navigation as receding-horizon diffusion planning, where the policy jointly predicts a K-step action chunk but executes only the first action before replanning from the next observation. This plan-K, execute-one strategy treats predicted future actions as auxiliary planning targets while preserving closed-loop visual feedback. Finally, we introduce LiteStop, a lightweight termination module that estimates the stop probability directly from the action logits at the initial all-mask state, thereby decoupling explicit termination from action generation.

Together, these components establish a causal closed-loop cycle of observation, memory retrieval, diffusion planning, termination assessment, execution, and memory update. Experiments on the OpenFly benchmark demonstrate consistent improvements in both seen and unseen environments. DreamFly achieves 32.04%/29.46% SR and 28.22%/23.54% SPL on the test-seen/test-unseen splits, respectively, outperforming all compared methods on both metrics while also attaining the lowest navigation error. These results highlight the effectiveness of jointly modeling historical context, future action structure, and explicit termination for aerial vision-language navigation.

Introduction

Vision-and-Language Navigation (VLN) requires embodied agents to interpret natural-language instructions and navigate to specified goals using sequentially acquired visual observations. In contrast to static visual recognition and single-step action classification, VLN is inherently a partially observable, closed-loop sequential decision problem: an action taken at the current step affects the agent's subsequent state and visual observation, while newly acquired environmental evidence updates its estimates of task progress, spatial context, and direction of travel. A reliable VLN policy therefore requires more than vision–language grounding at an isolated decision step. It must maintain temporal consistency across historical visual evidence, the current observation, planned future actions, and executed actions throughout its interaction with the environment.

Aerial vision-language navigation extends VLN to unmanned aerial vehicles (UAVs), requiring agents to navigate autonomously through large-scale 3D environments by following natural-language instructions. However, aerial VLN cannot be treated as a direct transfer of conventional ground-based VLN to a flying platform. Ground agents typically move on approximately two-dimensional traversable surfaces, whereas UAVs must jointly coordinate horizontal displacement, vertical motion, and viewpoint adjustment. Changes in altitude affect not only the UAV's spatial position but also the spatial extent of the visible scene, the apparent scale of landmarks, the level of visible detail, and obstacle visibility, thereby tightly coupling horizontal and vertical navigation decisions. Moreover, urban aerial environments often span large areas and contain complex 3D structures and substantial visual occlusion, such that a single egocentric observation captures only a limited portion of the surrounding scene. Consequently, local decision errors can compound over time. These characteristics impose stringent requirements on historical context modeling, 3D spatial reasoning, cross-modal grounding, and online error correction in aerial VLN.

Early VLN methods commonly relied on sequence-to-sequence architectures, typically implemented as recurrent policies, to autoregressively predict the next navigation action from the language instruction, current visual observation, and recurrent hidden state. With advances in vision–language pretraining and Transformer-based architectures, subsequent studies developed recurrent vision–language Transformers, history-aware representations, and explicit environmental memories to support reasoning across successive decision steps. For example, Recurrent VLN-BERT maintains a recurrent state representation, whereas HAMT employs a hierarchical Transformer to encode navigation history, thereby facilitating multimodal reasoning over extended trajectories. In aerial VLN, recent approaches have further explored multidirectional view selection, bird's-eye-view feature maps, keyframe selection, and compression of historical visual observations to mitigate restricted egocentric visibility, redundant observations, and difficulties in tracking navigation state. Together, these studies underscore the importance of historical information for navigation. However, historical modeling depends not only on how past observations are represented, but also on precisely when they become available to the policy during closed-loop interaction.

More recently, vision–language–action (VLA) models have emerged as a promising paradigm for language-conditioned end-to-end control. By formulating actions as discrete tokens or continuous values, VLA models adapt pretrained vision–language models to generate robot actions conditioned on language instructions and visual observations. Action chunking captures short-horizon temporal structure by jointly predicting a sequence of actions, while diffusion-based policies model multimodal action distributions through iterative denoising and can be deployed with receding-horizon replanning. Dream-VLA, for example, employs a diffusion Transformer to jointly predict action chunks, thereby capturing dependencies among actions over a short future horizon. Recent studies of aerial navigation have likewise explored end-to-end VLA policies, diffusion-based action modeling, and joint world–action prediction, highlighting the potential of explicitly predicting future states or action sequences for navigation in complex 3D environments. Several of these approaches seek to enhance planning by forecasting future world states or visual observations. However, how to exploit dependencies among predicted future actions over a short horizon while retaining closed-loop replanning from newly acquired observations remains underexplored.

Despite recent advances in historical modeling, 3D reasoning, and multi-step action prediction, three temporal aspects of aerial VLN remain insufficiently addressed within a unified closed-loop decision process. First, an online historical memory requires an explicit temporal boundary. Here, causal refers to temporally ordered information access rather than causal relations between variables: at decision step t, historical information is restricted to observations acquired before step t. Accordingly, the historical memory M<t is read before the current observation is written to it, preventing current-step information from entering the historical branch. Applying this read-before-write ordering during both training and deployment gives M<t a well-defined temporal boundary and ensures consistent information availability across the two phases. This temporal notion of causality should not be conflated with covariate shift between expert and policy-induced state distributions in imitation learning. It does not mitigate the covariate shift arising from policy execution; rather, it constrains when observations enter the historical memory.

Second, multi-step action prediction should provide lookahead for the current decision without sacrificing closed-loop execution. Replanning after each executed action allows the agent to exploit dependencies among predicted future actions without committing to an open-loop action chunk, thereby retaining responsiveness to newly acquired observations. Third, termination carries markedly different consequences from ordinary motion actions. Motion errors can often be corrected through subsequent actions, whereas a premature stop irreversibly ends the navigation episode. Treating termination and motion actions under the same prediction and calibration mechanism can therefore obscure the asymmetric risks of premature and delayed stopping. Together, these considerations motivate the explicit coordination of historical information, future-action planning, and termination within a temporally consistent closed-loop decision process.

To address these issues, we propose DreamFly, a temporally consistent, closed-loop aerial VLN framework built on discrete diffusion-based action generation. First, DreamFly introduces a causally aligned historical memory constructed exclusively from visual observations acquired before the current decision step. Under a read-before-write protocol, M<t is held fixed during decision step t and updated with the current observation only after the current decision, for use at subsequent steps. The resulting historical representation is fused with the current visual features through a lightweight memory-conditioning module, allowing the policy to reason jointly over the language instruction, historical visual evidence, and current observation.

Building on the memory-conditioned representation, DreamFly performs receding-horizon diffusion planning by jointly predicting a chunk of K discrete actions that comprises the current action and captures dependencies among actions over a short future horizon. During online inference, DreamFly follows a plan-K, execute-one strategy: it executes only the first action and replans upon receiving the next observation. In this way, the future positions in the action chunk serve as auxiliary prediction targets for the current decision, while execution remains closed loop.

Finally, we introduce LiteStop, a lightweight module that explicitly predicts termination. Rather than treating termination as an ordinary motion action, LiteStop estimates the probability of stopping from the action-token logit grid Ht extracted from the diffusion policy at its initial all-mask state. LiteStop is trained separately while the navigation policy remains frozen, providing a dedicated termination objective without modifying the learned motion policy. Together, causally aligned historical memory, receding-horizon diffusion planning, and explicit termination form a temporally consistent, closed-loop navigation process.

We make the following three main contributions:

1. We introduce a causally aligned historical memory for aerial VLN with an explicit temporal information boundary. DreamFly constructs its historical memory exclusively from observations acquired before the current decision step and adopts a read-before-write protocol that prevents current-step information from entering the historical branch. A lightweight gated cross-attention module selectively integrates task-relevant historical evidence into the current visual representation, thereby supporting temporally consistent reasoning under partial observability.

2. We propose receding-horizon diffusion planning that exploits dependencies among predicted future actions while preserving closed-loop control. DreamFly jointly predicts the current action and a short sequence of future actions as a chunk of K discrete actions, using valid-prefix supervision and horizon-aware loss weighting to emphasize the immediately executable action. During inference, DreamFly executes only the first action and replans upon receiving the next observation, allowing the predicted future actions to serve as auxiliary planning targets while preserving closed-loop execution.

3. We develop LiteStop, an explicit and decoupled termination module for aerial VLN. Rather than coupling termination with motion-action generation, LiteStop estimates the probability of stopping from the action logits produced by the diffusion policy at its initial all-mask state and is trained separately while the navigation policy remains frozen. This decoupling provides a dedicated termination objective without modifying the learned motion policy.

Method

Problem Formulation

Given a natural-language instruction I = (w1, w2,..., wNI), an aerial agent starts from an initial pose s0 = (p0, ψ0) in a 3D environment, where p0 = (x0, y0, z0) denotes its position and ψ0 denotes its heading. At each decision step t, the agent receives an egocentric RGB observation Ot and selects an action at from a discrete action space A. Executing at moves the agent to a new pose st+1 and produces the next observation Ot+1. The action space consists of Stop, forward motion with different step sizes, left and right turns, vertical movement, and lateral movement. The navigation episode terminates when the agent selects Stop or reaches the maximum number of decision steps. Given the ground-truth destination p*, an episode is considered successful if the Euclidean distance between the agent's final position pT and the destination is less than 20 m: ∥pT − p*∥2 < 20 m.

Framework Overview

DreamFly is an aerial vision-language navigation framework built upon Dream-VLA. We adopt Dream-VLA as the policy backbone because its perception-to-action formulation aligns naturally with aerial VLN, directly mapping visual observations and language instructions to executable actions. Compared with autoregressive VLA backbones, Dream-VLA employs bidirectional diffusion modeling to support action chunking and parallel action prediction, providing a suitable foundation for receding-horizon planning.

Unlike the original VLA setting, aerial navigation requires retaining historical visual evidence and continually replanning as new observations arrive. DreamFly therefore introduces three key designs. First, a causally aligned historical memory M<t is constructed only from observations acquired before the current decision step and conditions the current visual representation, preserving a well-defined temporal information boundary while retaining previously observed landmarks. Second, DreamFly performs receding-horizon diffusion planning by jointly predicting a K-step action chunk. The predicted future actions provide short-horizon planning structure without being committed to consecutive execution. If termination is not triggered, only the first action is executed before the policy replans from the next observation. Third, LiteStop estimates termination from the initial all-mask action logits and provides a separate pre-action termination decision. Together, these components establish a temporally consistent closed-loop process that integrates historical reasoning, receding-horizon future-action planning, and explicit termination control.

Causally Aligned Historical Memory

During continuous aerial navigation, previously observed landmarks and scene cues may leave the current field of view while remaining relevant to subsequent decisions. Retaining all past observations would preserve such information, but would also introduce substantial redundancy and cause the visual input to grow with trajectory length. To maintain useful historical evidence under a bounded input budget, DreamFly compresses instruction-relevant information from past observations into a fixed-capacity historical memory. A short candidate FIFO and active tracks are maintained internally to accumulate evidence across recent observations, while only the long-term memory slots are exposed to the navigation policy.

At decision step t, let Oτ denote the observation acquired at step τ. The historical memory available to …

Based on this paper, here are the specific improvements you can make to AI systems and what the improved systems can do:

1. Implement causally aligned historical memory with read-before-write protocol

• Construct memory exclusively from observations preceding the current decision step

• Use a gated cross-attention module to selectively retrieve task-relevant historical evidence

• Maintain fixed-capacity memory slots with anchor/prototype representations and validity masks

2. Adopt receding-horizon diffusion planning with plan-K, execute-one strategy

• Jointly predict a K-step action chunk using bidirectional diffusion, but execute only the first action

• Use valid-prefix supervision and horizon-aware loss weighting (γ=0.7) to emphasize the immediately executable action

• Apply deterministic geometric kernel for context-aware reweighting during training

3. Integrate decoupled termination control (LiteStop)

• Extract stop probability from initial all-mask action logits before diffusion generation completes

• Train termination head separately while freezing the main policy

• Use positive-class weighting (λ+=4.0) to handle class imbalance

• Combine LiteStop decision with action-level Stop condition via logical OR

4. Apply staged optimization rather than joint training

• Train memory-conditioned action policy first with horizon-aware objective

• Freeze the complete policy, then train only the termination module

• This prevents interference between objectives and enables modular improvements

Navigation and Control:

• Maintain temporal consistency across long trajectories without losing track of previously observed landmarks

• Make decisions with short-horizon lookahead while remaining responsive to new observations

• Terminate reliably at goal locations, reducing both premature stops and overshooting

• Handle partial observability by retrieving relevant historical context adaptively

Robustness and Generalization:

• Operate effectively in unseen environments (29.46% SR on test-unseen vs. 32.04% on test-seen)

• Maintain performance across varying initial distances from goals

• Correct navigation errors through replanning rather than committing to open-loop action sequences

Efficiency:

• Reduce computational overhead by using a lightweight termination module that reuses existing representations

• Avoid redundant processing by discarding unpredicted future actions and replanning only when needed

• Achieve lower navigation error (NE) compared to baselines while maintaining path efficiency (SPL)

Decision Quality:

• Distinguish between motion errors (correctable) and termination errors (irreversible)

• Exploit dependencies among future actions without sacrificing closed-loop feedback

• Adapt memory retention based on instruction relevance rather than storing all past observations