| --- |
| license: mit |
| library_name: pointmapvla |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| - vision-language-action |
| - pointmap |
| - qwen3.5 |
| - flow-matching |
| - robocasa |
| --- |
| |
| # PointmapVLA-RoboCasa |
|
|
| PointmapVLA fine-tuned on the 24 RoboCasa kitchen tasks (MimicGen-300 demonstrations). |
| This is the checkpoint behind the RoboCasa result of the paper. Code, recipes and the |
| evaluation harness: [PointmapVLA repository](https://github.com/DAVIAN-Robotics/PointmapVLA). |
|
|
| ## What it is |
|
|
| PointmapVLA is a vision-language-action model built on the Qwen3.5-4B vision-language |
| backbone with a **pointmap encoder** (a copy of the vision tower fed raw metric XYZ |
| pointmaps in the robot-base frame, features added patch-wise to the RGB tokens) and a |
| **32-layer layer-wise flow-matching DiT action expert** (4 Euler steps at inference). |
| 6.08 B parameters, bf16. |
|
|
| This checkpoint predicts chunks of 16 RoboCasa actions (15-D) from three camera views |
| (left, right, wrist), the instruction and an 18-D state, from which an 11-D end-effector |
| anchor is fed to the action expert. |
|
|
| ## Provenance |
|
|
| * Initialised from [`DAVIAN-Robotics/PointmapVLA-Pretrained`](https://huggingface.co/DAVIAN-Robotics/PointmapVLA-Pretrained) |
| (Stage 1 + Stage 2 pretraining on 76.3 M robot frames and 10.7 M 3D spatial VQA samples). |
| * Fine-tuned for 20,000 optimizer steps, global batch 128 (4 GPUs x 32), AdamW, cosine |
| schedule with 1,000 warm-up steps, learning rates 1e-5 (backbone, pointmap encoder) and |
| 1e-4 (action expert, projections), DeepSpeed ZeRO-2, bf16. |
| * Data: [`DAVIAN-Robotics/robocasa-MG_300-r6`](https://huggingface.co/datasets/DAVIAN-Robotics/robocasa-MG_300-r6) |
| + [`DAVIAN-Robotics/robocasa-MG_300-pointmap`](https://huggingface.co/datasets/DAVIAN-Robotics/robocasa-MG_300-pointmap) |
| (7,200 episodes, 2.08 M frames, 3 cameras). |
| * On load from the pretrained checkpoint the four action-head tensors whose shape depends |
| on the layout were remapped (shared end-effector/gripper slots copied, new slots fresh); |
| all other weights transferred unchanged. |
| * Prompt: `Task: {instruction} Mode: <end_effector> Pointmap_origin: <robot>` with |
| `enable_thinking: false` (an explicit empty `<think>` block). |
| * Exact config: `examples/simBenchmarks/Robocasa_kitchen/train_files/pointmapvla_robocasa_kitchen.yaml` |
| in the repository (`_80gb.yaml` is the same recipe with gradient accumulation). |
|
|
| ## Files |
|
|
| ``` |
| config.yaml architecture and the data recipe (release schema) |
| dataset_statistics.json action/state mean/std/min/max/quantiles used for (un)normalisation |
| base_vlm/ Qwen3.5-4B config, tokenizer and processor only; no weights |
| checkpoints/ |
| model-00001-of-00003.safetensors |
| model-00002-of-00003.safetensors |
| model-00003-of-00003.safetensors ~11 GB in total, bf16 |
| model.safetensors.index.json |
| ``` |
|
|
| State-dict key groups: `qwen_vl_interface.model.*` (backbone), `pointmap_encoder.*`, |
| `action_model.*` (DiT expert), `project_layers.*` (per-layer projections). The language |
| model head is tied to the token embedding and stored once; the index records it under |
| `metadata.tied_weights` and the loader re-ties it. |
|
|
| ## How to load |
|
|
| ```python |
| from starVLA.model.framework.VLM4A.PointmapVLA import PointmapVLA |
| |
| model = PointmapVLA.from_pretrained("DAVIAN-Robotics/PointmapVLA-RoboCasa") |
| out = model.predict_action(examples, noise_seed=0) # {"normalized_actions": (B, 16, 15)} |
| ``` |
|
|
| `examples[i]` is a dict with `image` (3 HWC uint8 arrays, 224 x 224), `lang`, `state` |
| (`(1, 18)` float32, normalised), `pointmap` (`(3, 256, 256, 3)` float16 metres, |
| robot-base frame) and `pointmap_valid` (`(3, 256, 256)` bool). Un-normalise with |
| `dataset_statistics.json`. In practice, run the policy server from the repository, which |
| does the un-normalisation and the r6 -> axis-angle conversion: |
|
|
| ```bash |
| python deployment/model_server/server_policy.py --ckpt DAVIAN-Robotics/PointmapVLA-RoboCasa --port 20000 --use_bf16 |
| ``` |
|
|
| ## Evaluation |
|
|
| Protocol: 24 tasks x 50 episodes, max 720 steps, action chunk 16 executed in full, 4 Euler |
| steps, environment seed 42 (episode i uses `42*100000 + i`), three repetitions with policy |
| noise seeds 0, 1, 2; the number is the mean (± sd) over repetitions. One repetition alone |
| typically lands within about ±1.3 points of the mean. |
|
|
| ```bash |
| CKPT=DAVIAN-Robotics/PointmapVLA-RoboCasa GPUS=0,1 scripts/eval/run_eval_robocasa.sh |
| ``` |
|
|
| | Benchmark | Paper | Our reproduction | |
| |---|---|---| |
| | RoboCasa kitchen-24, success rate (%) | 78.2 | 77.8 ± 0.6 (3 repetitions) | |
|
|
| ## Intended use and limitations |
|
|
| * Simulation benchmark checkpoint for the RoboCasa kitchen-24 tasks (Panda-Omron, MG-300 |
| distribution). Not validated on physical robots. |
| * Layouts are fixed: action 15-D (EE position 3 | EE rotation r6 6 | gripper 1 | base |
| motion 4 | control mode 1), horizon 16; state 18-D (base position 3 | base quaternion 4 | |
| EE position 3 | EE rotation r6 6 | gripper 2). For another robot or layout, fine-tune |
| `PointmapVLA-Pretrained` instead. |
| * Requires metric depth with known intrinsics and camera-to-world extrinsics for the |
| pointmap input; the model was trained with pointmaps present. |
|
|
| ## License |
|
|
| MIT (code and weights of this release). Weights are derived from Qwen3.5-4B, distributed |
| under the Apache License 2.0. See `NOTICE` in the repository. |
|
|
| ## Citation |
|
|
| TBD. The paper is under double-blind review; the BibTeX entry and author list will be |
| added at camera-ready. |
|
|
