---
license: apache-2.0
tags:
  - robotics
  - vision-language-action
  - grounding
---

# Galahad — 7-axis merged grounded VLA (single weight)

> [Project page](https://xn--7xa.monster/Galahad/) · [Code + battery](https://github.com/phi-monster/Galahad) · [Datasets](https://huggingface.co/phi-monster) · [Dual-output weight](https://huggingface.co/phi-monster/Galahad-object-unified)
>
> Paper: **Instruction Blindness in Vision–Language–Action Policies: Diagnosis and a Low-Rank Data Cure** — [PDF](https://xn--7xa.monster/Galahad/static/galahad.pdf)

A single merged weight (base + folded LoRA, no adapter to attach) that follows the **referring expression** in the instruction rather than a memorised position or scene prior. Seven referent axes, all measured **on this exact artifact**.

---

## 🔴 READ THIS BEFORE LOADING — the bundled `norm_stats.json` is WRONG for this model

This repository ships `norm_stats.json` inherited from the uncured base `allenai/MolmoAct2-Think-LIBERO`. It is **byte-identical to the base's** (`md5 03084dfec9bb21f716100fea20f3d2c9`) and **must not be used to denormalise this model's actions.**

The merge wrote HF-transformers format and dropped the lerobot processor files that carry the trained normalisation statistics. Those files are now included in this repo and **are the only correct source**:

```
policy_preprocessor.json
policy_preprocessor_step_3_molmoact2_masked_normalizer.safetensors
policy_postprocessor.json
policy_postprocessor_step_1_molmoact2_masked_unnormalizer.safetensors
```

Load with the lerobot processor path:

```python
from lerobot.policies.factory import make_pre_post_processors
pre, post = make_pre_post_processors(cfg, pretrained_path="<path to this repo>")
```

**Do NOT** use `make_molmoact2_pre_post_processors(cfg)` — it reads `norm_stats.json` and silently applies the base's action quantiles.

**Failure signature if you get this wrong** (measured, so you can recognise it): this corpus has roll/pitch **identically zero** and a yaw span of 0.0116, while the base's LIBERO spans are 0.233 / 0.335 / 0.444. Denormalising through the base's statistics injects a **constant roll −0.103 and pitch −0.155 on every step** plus a 38× yaw gain — a continuously tumbling wrist. Because x/y/z spans differ by only 5-12%, **the arm still reaches within ~2 mm of the correct object and then never closes the grasp.** It looks like a motor failure; it is a normalisation failure.

### Second requirement: `ZERO_STATE=1`

Serve with `observation.state` zeroed **and** the `state_encoder` output layer zeroed. This model's generation runs away on real proprioception. Every number below was measured under this contract.

---

## Main table — all seven axes, measured on this weight

Contract for every cell: this weight · the processor files above · `ZERO_STATE=1`.

| axis | **SWAP-OBEY** (told to fetch a different in-scene object, does the EEF go to the newly-named one?) | **OCC** (both cameras blanked) | TASK (completion) |
|---|---|---|---|
| **object** | **198/200 = 99.0%** · true-target **0/200** | 0/8 = 0% | 160/200 = 80.0% |
| **spatial** | **OBEYED 187 / WENT_TRUE 0** | 0/40 = 0% | 110/200 = 55.0% |
| **goal** | **VARY 40/48 = 83.3%** (referent displaced 7-9 cm) | 0/24 = 0% | — |
| **colour** | **32/36 = 88.9%** [74.7, 95.6] | 0/36 = 0% | 8/36 = 22.2% |
| **negation** | **43/48 = 89.6%** [77.8, 95.5] | 0/48 = 0% | 8/48 = 16.7% |
| **category** | **39/50 = 78.0%** [64.8, 87.2] | 1/50 = 2.0% | 10/50 = 20.0% |
| **compositional** | **36/48 = 75.0%** [61.2, 85.1] | 0/48 = 0% | 1/48 = 2.1% |

**All seven axes ground (75-99%) and all seven are vision-gated (0-2%).**

### How to read these numbers honestly

- **SWAP-OBEY is the grounding measurement; TASK is not.** Under a swapped instruction, "true-target ≈ 0" alone is ambiguous — a drop could mean "followed the new name" *or* "just failed". The pair (true-target 0 **and** obeyed-name high) is what rules out the second reading.
- **compositional decomposes, and the split is the useful part:** `flip_colour` **21/24 = 87.5%** vs `flip_size` **15/24 = 62.5%**. The colour attribute is followed as well as the single-attribute axes; **size is the short leg.**
- **TASK is low on the RoboCasa axes by bench design, not by regression.** The published single-axis anchors on the same benches are TASK 26-51% against SWAP-OBEY 77-95%. This weight sits ~10-15 pp below those TASK anchors (a motor cost of unifying seven axes into one weight) while its grounding sits at or above them.
- **spatial's OBEY count is backed by the raw distance distribution** (n=200), because its two referents are identical bowls placed close together: `d_named` median **20.0 mm** vs `d_true` median **124.0 mm** = **6.20× separation**, only 4.5% of trials ambiguous.
- **The OCC zeros are not a stuck counter.** The same scorers emit non-zero under other conditions in the same runs (category's own OCC reads 1/50), so the collapse is a real measurement.
- **The low TASK is not a serving artifact.** Paired control, same weight / harness / cells, normaliser the only variable: an independently regenerated normaliser gives **8/36 vs 8/36 — identical**.

## Baselines for context

The uncured base is not a weak model — it is strong *and* shortcut-driven, which is the point:

- **spatial:** uncured base scores TASK 99% but follows a swapped spatial referent only **50% = exact chance for two bowls**. Its 99% is a canonical-bowl shortcut.
- **goal:** uncured base **31.2%** on displaced referents (it goes to the remembered position) vs **83.3%** here.

## Limitations

- Closed vocabulary. Referents outside the training vocabulary are not expected to work.
- TASK completion on the RoboCasa axes is motor-limited (see above); this weight trades some motor precision for grounding.
- `compositional`'s size attribute is measurably weaker than its colour attribute.
- Sim-trained (LIBERO-PRO / RoboCasa). No real-robot transfer claim.