Existing vision-and-language navigation (VLN) models primarily reason over past and current visual observations, while largely ignoring the future visual dynamics induced by actions. As a result, they often lack an effective understanding of the causal relationship between actions and how the visual world changes, limiting robust decision-making. Humans, in contrast, can imagine the near future by leveraging action-dynamics causality, which improves both environmental understanding and navigation choices. Inspired by this capability, we propose LatentPilot, a new paradigm that exploits future observations during training as a valuable data source to learn action-conditioned visual dynamics, while requiring no access to future frames at inference. Concretely, we propose a flywheel-style training mechanism that iteratively collects on-policy trajectories and retrains the model to better match the agent's behavior distribution, with an expert takeover triggered when the agent deviates excessively. LatentPilot further learns visual latent tokens without explicit supervision; these latent tokens attend globally in a continuous latent space and are carried across steps, serving as both the current output and the next input, thereby enabling the agent to dream ahead and reason about how actions will affect subsequent observations. Experiments on R2R-CE, RxR-CE, and R2R-PE benchmarks achieve new SOTA results, and real-robot tests across diverse environments demonstrate LatentPilot's superior understanding of environment-action dynamics in scene. Project page:https://abdd.top/latentpilot/
Figures & tables
Figure 1: Prior VLN pipelines optimize next-action prediction from past/current observations, leaving future observations in trajectories largely unused as supervision. LatentPilot uses future observations only as training-time privileged supervision to internalize action-conditioned visual dynamics while inference remains strictly causal and requires neither future frames nor an external world-model rollout.
Figure 2: Overview of LatentPilot. The vision encoder extracts visual tokens from the current observation, the LLM backbone predicts the action, and a lightweight Pilot module updates a propagated Pilot Token that is cached and reused as the Pilot slot input at the next step, enabling strictly causal dreaming-ahead.
Figure 3: PilotLoop: flywheel-style closed-loop training. Each round iterates rollout collection, expert takeover for deviation-aware correction, future-privileged target construction, and fine-tuning with joint action imitation and Pilot supervision.
Figure 4: Visibility matrix of LatentPilot.
Method
Observation
R2R Val-Unseen
RxR Val-Unseen
Pano.
Odo.
D.
SRGB
NE ↓
OS ↑
SR ↑
SPL ↑
NE ↓
SR ↑
SPL ↑
nDTW ↑
CMA [ 14 ]
✓
✓
✓
6.20
52.0
41.0
36.0
8.76
26.5
22.1
47.0
VLN-BERT [ 14 ]
✓
✓
✓
5.74
53.0
44.0
39.0
8.98
27.0
22.6
46.7
Sim2Sim [ 24 ]
✓
✓
✓
6.07
52.0
43.0
36.0
–
–
–
–
Ego2-Map [ 15 ]
✓
✓
✓
5.54
56.0
47.0
41.0
–
–
–
–
DreamWalker [ 47 ]
✓
✓
✓
5.53
59.0
49.0
44.0
–
–
–
–
Table 1: Comparison with state-of-the-art methods on VLN-CE R2R and RxR Val-Unseen splits. Pano, Odo and D respectively represent panoramic view, odometry and depth, SRGB denotes monocular RGB.
Method
R2R Validation Seen
R2R Validation Unseen
NE ↓
FR ↓
StR ↓
OS ↑
SR ↑
SPL ↑
NE ↓
FR ↓
StR ↓
OS ↑
SR ↑
SPL ↑
Train-free Map-based Exploration and Navigation
VLMaps [ 16 ]
–
–
–
–
–
–
6.98
23.00
0.00
20.00
20.00
12.70
Train on VLN-PE
Seq2Seq [ 25 ]
7.73
22.19
3.04
30.55
19.60
15.67
7.91
19.67
3.71
27.62
15.89
12.58
Seq2Seq+ [ 25 ]
7.54
26.11
5.59
31.93
19.58
15.13
7.64
21.82
5.12
30.47
18.13
14.06
Table 2: Evaluation Metrics on VLN-PE benchmark with physical locomotion controller. +: model is first trained on Habitat and fine-tuned on VLN-PE. † : model is trained with data augmentation.
Modality
R2R Validation Unseen
R2R Validation Seen
SR ↑
SPL ↑
NE ↓
OS ↑
SR ↑
SPL ↑
NE ↓
OS ↑
NaN
51.7
47.1
5.3
57.0
53.1
46.9
4.6
60.7
3D
50.3
45.6
5.4
55.8
51.5
45.1
4.9
59.9
Text
53.2
48.7
5.2
59.6
54.9
48.6
4.5
63.9
Vision(ours)
62.0
58.0
4.4
66.3
65.1
59.6
4.3
71.6
Table 3: Ablation study of supervision modality for the Pilot Token. NaN removes any extra Pilot supervision and optimizes only the action cross-entropy, serving as a baseline that tests whether the propagated latent becomes predictive by itself.
World Model
T/Act (ms) ↓
Mem ↓ (GB)
R2R Val-Seen
SR ↑
SPL ↑
NE ↓
OS ↑
Wan2.1 1.3B [ 46 ]
2040
41.5
56.6
51.7
5.1
64.5
CogVideoX1.5 5B [ 57 ]
5460
32.4
59.3
53.9
4.9
66.8
Ours
130
22.8
65.1
59.6
4.3
71.6
Table 4: Internalized future supervision (ours) vs. plug-in video world models on R2R Val-Seen. T/Act: ms per action; Mem: peak GPU memory (GB).
Figure 5: Performance over PilotLoop.
Figure 6: Latent collapse Analysis via PCA.
Figure 7: Real-world Cross-Embodiment Experiments: Wheeled and Quadruped Robots. LatentPilot successfully follows long, multi-step instructions on both Robots.