Vision-language navigation policy built on InternVL3.5-1B with a separate
StopHead and ProprioProjector. Trained on the VLNVerse coarse/fine training
set; evaluated on the official coarse/val_unseen 835-episode benchmark using
the vlnverse_emr evaluation framework.
Headline result
On the full VLNVerse coarse/val_unseen (835 episodes) with stop_threshold = 0.95:
Metric
Value
Success Rate (SR)
51.14%
SPL
49.22%
Oracle Success Rate (OSR)
64.79%
Navigation Error (NE)
3.727 m
nDTW
0.9445
Mean Trajectory Length
6.121 m
Comparison vs VLNVerse paper baselines
Reproduced inside the official vlnverse_emr framework on the same coarse/val_unseen
split. Baseline numbers from VLNVerse paper (arXiv:2512.19021, Table 3):
Method
SR ↑
SPL ↑
Δ vs RyWorld
CMA (VLN-CE)
32.15%
29.06%
−18.99 / −20.16
Seq2Seq
31.91%
29.68%
−19.23 / −19.54
HNR
36.02%
33.67%
−15.12 / −15.55
RDP
41.61%
37.53%
−9.53 / −11.69
GAMA (paper SOTA)
42.45%
38.89%
−8.69 / −10.33
RyWorld @ thr=0.95 (this model)
51.14%
49.22%
—
Architecture
Inputs: Outputs (per chunk position, chunk_size=4):
- RGB 256x256 (Isaac live or - Discrete head xattn: 4-way CE
pre-rendered training video) (0=Stop / 1=Forward / 2=TurnLeft / 3=TurnRight)
- Instruction text (formal variant) - Stop head xattn: BCE-with-pos_weight
- Proprio history N=8 keyframes soft target stop_proximity = exp(-d/tau)
(body-frame deltas [dx, dy, tau=4.33 aligned to success_radius=3 m
cos(dtheta), sin(dtheta)])
- Previous action class history
(decision-point keyframe selector)
Backbone: OpenGVLab/InternVL3_5-1B (InternViT-300M + Qwen3-0.6B, d_model=1024,
vision tower frozen, LoRA r=8 on language, mlp1 trainable)
Connector: ProprioProjector (continuous proprio -> 1024 embedding)
Detailed architecture & training in docs/RYWORLD_ARCHITECTURE.md of the source repo.
Per-segment performance
SR broken down by reference path length (shortest_path_length):
Path length (m)
n
SR
NE (m)
[ 0, 5)
151
66.9%
2.55
[ 5, 8)
360
59.4%
3.07
[ 8, 12)
226
42.9%
4.33
[12, 18)
96
14.6%
6.58
[18, 30)
2
50.0%
4.55
The drop on long paths (>12 m) is the dominant remaining gap; addressing it
likely requires either training-time long-horizon planning supervision or
larger forward_distance per high-level action.
Stop head behavior (151,740 chunk-positions)
Statistic
Value
stop_prob median
0.752
stop_prob p90
0.897
pathA fire (argmax==Stop, natural)
2.51%
pathB fire (threshold override)
0.68%
no-stop
96.81%
stop_threshold=0.95 was selected via a 4-point sweep (0.88/0.92/0.95/0.97) on
a 30-episode smoke subset before the full run. Higher thresholds (0.97+) cause
overshoot regressions on the long-path segment; 0.95 is the empirical sweet spot.
