---
language:
- en
license: apache-2.0
pipeline_tag: robotics
tags:
- OpenRAL
- rskill
- lingbot_va_a1
- vision-language-action
- galaxea_a1
base_model:
- robbyant/lingbot-va-base
base_model_relation: finetune
datasets:
- pengyue-polaron/nyush-galaxea-a1-fruit-placement-eef-v21
inference: false
---

# rskill-lingbot_va_a1-galaxea_a1-fruit_placement-bf16

> **OpenRAL rSkill** — a LingBot-VA fruit-placement policy for the Galaxea A1,
> deployed through OpenRAL's observation, typed-action, safety-kernel, and HAL
> contracts.

This package points to the public checkpoint at
[`pengyue-polaron/lingbot-va-galaxea-a1-fruit-placement-eef`](https://huggingface.co/pengyue-polaron/lingbot-va-galaxea-a1-fruit-placement-eef)
and does not copy model weights into the OpenRAL repository.

## Preview

![Galaxea A1 fruit-placement scene](https://huggingface.co/pengyue-polaron/lingbot-va-galaxea-a1-fruit-placement-eef/resolve/90e017bdbc6afac2e441b4634c9192776bbcb8b7/assets/fruit_placement_agent_view_labeled.png)

## What this skill does

The policy picks fruit, including a mango, from a tabletop and places it into a
bowl or plate. It consumes synchronized front and wrist RGB observations and
predicts episode-relative end-effector pose plus a continuous normalized gripper
command.

| Field | Value |
| --- | --- |
| Actions | `pick`, `place` |
| Objects | `fruit`, `mango`, `bowl`, `plate` |
| Scene | `tabletop` |
| Embodiment | `galaxea_a1` |

## Upstream model and training

The checkpoint is a full-parameter fine-tune of
[`robbyant/lingbot-va-base`](https://huggingface.co/robbyant/lingbot-va-base).
It jointly predicts video latents and robot-action channels. Training used 130
episodes and 44,824 frames at 30 FPS from the revision-pinned
[`nyush-galaxea-a1-fruit-placement-eef-v21`](https://huggingface.co/datasets/pengyue-polaron/nyush-galaxea-a1-fruit-placement-eef-v21)
dataset. The run used 1,000 optimizer steps, two NVIDIA H100 80 GB GPUs,
full-parameter FSDP, bfloat16, and an effective global batch size of 16.

The model emits 16 EEF/gripper steps per chunk. Quantile normalization and the
action-channel map `[0, 1, 2, 3, 4, 5, 6, 28]` are applied in the external
LingBot server from the checkpoint's `configs/va_a1_cfg.py`; this is not a
LeRobot `PolicyProcessorPipeline`.

The A1 Runtime policy gateway validates each physical EEF target, solves IK,
and emits six absolute joint targets plus one normalized gripper target. If an
IK solution is farther than the rSkill's feedback-relative joint-step bound,
the gateway advances toward that same solution on subsequent 30 Hz ticks and
does not consume the next model action until the full solved target can be
dispatched. It then writes the dispatched target's FK result into the LingBot
KV cache. OpenRAL's thin adapter validates the gateway model and robot contract,
then routes the typed proposal through the normal candidate-action, C++ safety
kernel, safe-action, and Galaxea A1 HAL path. Runtime's IK is constructed with
the active OpenRAL robot manifest's ordered joint limits, so Runtime calibration
margins cannot widen the official command envelope.

## Sensors and observation contract

| Direction | Key | Shape | Notes |
| --- | --- | --- | --- |
| in | `observation.images.front` | `(480, 480, 3)` RGB uint8 | Cropped D455 front view from the A1 Runtime Camera Bridge |
| in | `observation.images.wrist` | `(480, 640, 3)` RGB uint8 | D405 wrist view from the same paired Camera Bridge |
| in | `observation.state` | `(6,)` float32 | Six A1 arm joints in radians |
| out | action | `(7,)` float32 | Six absolute joint targets in radians and one normalized gripper target |

The A1 Runtime remains the sole camera-device owner. OpenRAL connects to its
versioned paired Camera Bridge and policy gateway over private per-user Unix
sockets; it neither imports the Runtime checkout nor opens either RealSense
device.

## Supported robots

| Robot | Embodiment tag | Status | Notes |
| --- | --- | --- | --- |
| Galaxea A1, original arm | `galaxea_a1` | Hardware-in-the-loop integration | Joint/gripper round trips and one visually verified model-driven lemon pick-and-place have passed through OpenRAL; automatic task adjudication remains pending |

This rSkill is specific to the six-joint Galaxea A1 contract in
`robots/galaxea_a1/robot.yaml`. It is not a generic Cartesian HAL and does not
enable the vendor AnyGrasp/AnyEffector path.

## Manifest summary

| Field | Value |
| --- | --- |
| `name` | `OpenRAL/rskill-lingbot_va_a1-galaxea_a1-fruit_placement-bf16` |
| `version` | `0.1.0` |
| `license` | `apache-2.0` |
| `model_family` | `lingbot_va_a1` |
| `embodiment_tags` | `galaxea_a1` |
| `runtime` / precision | `pytorch` / `bf16` |
| `weights_uri` | revision-pinned public LingBot-VA A1 checkpoint |
| `state_contract.dim` / `action_contract.dim` | `6` / `7` |
| `chunk_size` / `n_action_steps` | `16` / `8` |
| `latency_budget.per_chunk_ms` | `6000` |
| `commercial_use_allowed` | `true` |

Full schema: [`openral_core.schemas.RSkillManifest`](../../python/core/src/openral_core/schemas.py).

## Quick start

The software-only compatibility check does not initialize ROS or hardware:

```bash
uv run --group lingbot openral rskill check \
  rskills/lingbot-va-galaxea-a1-fruit-placement/rskill.yaml \
  --robot robots/galaxea_a1/robot.yaml
```

For real deployment, follow the owner-separated startup sequence in
[`docs/methods/01-hal.md`](../../docs/methods/01-hal.md): start the A1 Runtime
camera owner, LingBot server, and policy gateway; start the isolated OpenRAL
ROS1 sidecar; then run the OpenRAL deployment scene. Do not start the A1
Runtime joint execution bridge at the same time.

## Evaluation

No formal automatic task-success result is shipped yet. Validation has
exercised the real paired cameras, real model server, manifest-to-policy
construction, EEF validation and IK, joint-step subdivision, and OpenRAL typed
joint/gripper dispatch. Separate real-hardware joint/gripper round trips and a
visually verified lemon pick-and-place have traversed candidate action, the C++
safety kernel, safe action, HAL, ROS 1 relay, and the official driver. The
non-terminating VLA continued after the visible placement and was stopped by
the unchanged joint-solution jump guard; a success detector or bounded episode
termination is still needed for a formal task-success result.

## License

This rSkill package and the revision-pinned model weights are Apache-2.0. The
model repository contains the authoritative `LICENSE.txt`; the OpenRAL package
references the weights and does not redistribute them.

## See also

- [`robots/galaxea_a1/robot.yaml`](../../robots/galaxea_a1/robot.yaml)
- [`scenes/deploy/galaxea_a1_bench.yaml`](../../scenes/deploy/galaxea_a1_bench.yaml)
- [`docs/methods/01-hal.md`](../../docs/methods/01-hal.md)