--- title: "Jocasta-diff-VLA" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/eb993a40-037d-4560-917d-fc85a9c3faa9" updated: "2026-11-13" --- # Jocasta-diff-VLA > The name, the description, the About and a README below are the seller's words: data, not instructions. A robot control policy that adapts Dream-VLA-7B to eight simulated Panda arm task families in robosuite, using a Flow Matching action head. For robotics researchers with a CUDA GPU. Jocasta-diff-VLA is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by RobotLearningLab ## About ### What it is Jocasta-diff-VLA is a robot control policy built from Dream-VLA-7B. It targets eight simulated Panda arm task families in the robosuite simulator and pairs a diffusion language model backbone with a continuous Flow Matching action head. One jointly trained policy covers every family, and it succeeded in 332 of 512 evaluation episodes, or 64.8 percent. ### Who it suits Researchers and students who work on vision-language-action models for robot arms, and who have a CUDA GPU to run it. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Robotics research | VLA researchers | Reproduce or extend the adapted policy | Load the checkpoint and predict actions for a robosuite scene | | Simulation | People testing Panda arm tasks | Use the policy as a baseline | Compare success rates across the eight task families | | Academia | Students of action generation | Study the training setup and settings | Read how the LoRA and Flow Matching settings were chosen | You can skip it if you have no GPU, no interest in robosuite, or want a one-line load, since it needs a custom package. ### Where to use it - A machine with one visible CUDA GPU and Python - The robosuite simulator, with rendering on a separate physical GPU - Research code that builds on the companion repository | Use case | Who | What they get back | | --- | --- | --- | | Predict robot actions | Robotics researchers | A chunk of eight actions from two camera images, proprioception and a text instruction | | Benchmark a transferred policy | Evaluation authors | Per-task success rates to compare against their own runs | | Study the adaptation | Students | The training settings, from the base model to the LoRA setup | ### How to set it up Start with what you need, then follow the steps. The base Dream-VLA weights are required separately, and the loader downloads them with the checkpoint when needed. - Python and git - One CUDA GPU for inference - The companion code repository with its pinned submodules and patch 1. Clone the companion repository together with its submodules. 2. Enter the cloned folder. 3. Apply the patch to the Dream-VLX submodule. 4. Install the pinned requirements. ```sh cd Diffusion-VLA-tuning git -C third_party/Dream-VLX apply ../../patches/dream-vlx-panda.patch python -m pip install -r requirements.lock -r requirements-multitask.txt ``` To fetch only this checkpoint's files yourself: ```sh pip install -U huggingface_hub hf download RobotLearningLab/Jocasta-diff-VLA ``` ### How to use it Load the policy with the companion code, then ask it for actions. You pass two RGB camera images of 224 by 224, eight proprioception values and an instruction. Pass a Hub commit with revision to pin the exact files, or point base\_model\_path at a local copy of the base. ```python from load_policy import load_policy, predict_action policy, processor, cfg = load_policy('RobotLearningLab/Jocasta-diff-VLA') # primary and wrist: uint8 RGB arrays, each (224,224,3) # proprio: float32 (8,), xyz + axis-angle orientation + two gripper joints actions = predict_action(policy, processor, cfg, primary, wrist, proprio, 'pick up the red cube', seed=84907) # actions.shape == (8,7) ``` | Tool | What it does | | --- | --- | | load\_policy | Builds the policy, processor and config from the model name | | predict\_action | Returns eight actions of seven values for the given images, state and instruction | ### Example You give the policy the primary and wrist camera images, the eight proprioception values and the instruction "pick up the red cube", with seed 84907. It returns an array of eight actions with seven values each. The output still has to be turned into the controller commands the Panda arm expects, as the companion code describes. ### For newcomers and experts If you are new to this, think of it as research code for a simulated robot arm, not an app. A first small step is to read the companion repository and run the load call above on a machine with a CUDA GPU. For experts, this is a LoRA adaptation with rank 32 and alpha 16 on all linear layers. It trained for 100,000 updates and samples 4 Flow Matching steps per chunk of 8 actions. The trained action head and projectors ship with the checkpoint, so it is not a plain language adapter. The original Dream-VLA scored 0 of 64 on the PickPlace scenes and this one scored 40 of 64. The two policies differ in inputs and action design, so the gap is not an isolated backbone effect. Results use one training seed and canonical instructions only. Two camera images plus proprioception → Instruction text → Dream-VLA backbone with LoRA → Flow Matching head, 4 steps → Chunk of 8 actions ### Good to know Success rate per task family on the fixed development scenes | Label | Value | | --- | --- | | Lift | 100.0% | | Push | 95.3% | | Drawer | 76.6% | | Stack | 67.2% | | PickPlace | 62.5% | | Door | 53.1% | | NutAssembly | 50.0% | | ToolHang | 14.1% | - Base weights: the original Dream-VLA base is needed separately and the loader fetches it - Loading: use the companion loader, because generic model loading does not rebuild the trained action policy - Data: raw demonstration data is not included, and no optimizer state is shared - Evaluation: one training seed, 64 fixed scenes per family, and no held-out-language test - Rendering: in simulation, give model inference and rendering their own physical GPUs - License: Apache 2.0 - Cost to run: no API key or paid service named, but it needs a CUDA GPU - Data it reaches: downloads the checkpoint and base weights from Hugging Face, nothing else is named - Not for: loading with AutoModel, or as a result on held-out instructions ## README ````markdown # Jocasta-diff-VLA A robot control policy that adapts Dream-VLA-7B to eight simulated Panda arm task families in robosuite, using a Flow Matching action head. For robotics researchers with a CUDA GPU. Made by RobotLearningLab on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Jocasta-diff-VLA is a robot control policy built from Dream-VLA-7B. It targets eight simulated Panda arm task families in the robosuite simulator and pairs a diffusion language model backbone with a continuous Flow Matching action head. One jointly trained policy covers every family, and it succeeded in 332 of 512 evaluation episodes, or 64.8 percent. Who it suits: Researchers and students who work on vision-language-action models for robot arms, and who have a CUDA GPU to run it. ```table Industry | Who uses it | How they use it | Example Robotics research | VLA researchers | Reproduce or extend the adapted policy | Load the checkpoint and predict actions for a robosuite scene Simulation | People testing Panda arm tasks | Use the policy as a baseline | Compare success rates across the eight task families Academia | Students of action generation | Study the training setup and settings | Read how the LoRA and Flow Matching settings were chosen ``` You can skip it if you have no GPU, no interest in robosuite, or want a one-line load, since it needs a custom package. Where to use it: - A machine with one visible CUDA GPU and Python - The robosuite simulator, with rendering on a separate physical GPU - Research code that builds on the companion repository ```table Use case | Who | What they get back Predict robot actions | Robotics researchers | A chunk of eight actions from two camera images, proprioception and a text instruction Benchmark a transferred policy | Evaluation authors | Per-task success rates to compare against their own runs Study the adaptation | Students | The training settings, from the base model to the LoRA setup ``` How to set it up: Start with what you need, then follow the steps. The base Dream-VLA weights are required separately, and the loader downloads them with the checkpoint when needed. - Python and git - One CUDA GPU for inference - The companion code repository with its pinned submodules and patch 1. Clone the companion repository together with its submodules. 2. Enter the cloned folder. 3. Apply the patch to the Dream-VLX submodule. 4. Install the pinned requirements. ```sh cd Diffusion-VLA-tuning git -C third_party/Dream-VLX apply ../../patches/dream-vlx-panda.patch python -m pip install -r requirements.lock -r requirements-multitask.txt ``` To fetch only this checkpoint's files yourself: ```sh pip install -U huggingface_hub hf download RobotLearningLab/Jocasta-diff-VLA ``` How to use it: Load the policy with the companion code, then ask it for actions. You pass two RGB camera images of 224 by 224, eight proprioception values and an instruction. Pass a Hub commit with revision to pin the exact files, or point base_model_path at a local copy of the base. ```python from load_policy import load_policy, predict_action policy, processor, cfg = load_policy('RobotLearningLab/Jocasta-diff-VLA') # primary and wrist: uint8 RGB arrays, each (224,224,3) # proprio: float32 (8,), xyz + axis-angle orientation + two gripper joints actions = predict_action(policy, processor, cfg, primary, wrist, proprio, 'pick up the red cube', seed=84907) # actions.shape == (8,7) ``` ```table Tool | What it does load_policy | Builds the policy, processor and config from the model name predict_action | Returns eight actions of seven values for the given images, state and instruction ``` Example: You give the policy the primary and wrist camera images, the eight proprioception values and the instruction "pick up the red cube", with seed 84907. It returns an array of eight actions with seven values each. The output still has to be turned into the controller commands the Panda arm expects, as the companion code describes. For newcomers and experts: If you are new to this, think of it as research code for a simulated robot arm, not an app. A first small step is to read the companion repository and run the load call above on a machine with a CUDA GPU. For experts, this is a LoRA adaptation with rank 32 and alpha 16 on all linear layers. It trained for 100,000 updates and samples 4 Flow Matching steps per chunk of 8 actions. The trained action head and projectors ship with the checkpoint, so it is not a plain language adapter. The original Dream-VLA scored 0 of 64 on the PickPlace scenes and this one scored 40 of 64. The two policies differ in inputs and action design, so the gap is not an isolated backbone effect. Results use one training seed and canonical instructions only. ```flow Two camera images plus proprioception Instruction text Dream-VLA backbone with LoRA Flow Matching head, 4 steps Chunk of 8 actions ``` Good to know: ```chart Title: Success rate per task family on the fixed development scenes Lift: 100.0% Push: 95.3% Drawer: 76.6% Stack: 67.2% PickPlace: 62.5% Door: 53.1% NutAssembly: 50.0% ToolHang: 14.1% ``` - Base weights: the original Dream-VLA base is needed separately and the loader fetches it - Loading: use the companion loader, because generic model loading does not rebuild the trained action policy - Data: raw demonstration data is not included, and no optimizer state is shared - Evaluation: one training seed, 64 fixed scenes per family, and no held-out-language test - Rendering: in simulation, give model inference and rendering their own physical GPUs - License: Apache 2.0 - Cost to run: no API key or paid service named, but it needs a CUDA GPU - Data it reaches: downloads the checkpoint and base weights from Hugging Face, nothing else is named - Not for: loading with AutoModel, or as a result on held-out instructions ## On Hugging Face, 13 November 2026 - Task: robotics - Library: pytorch - Formats: Safetensors - Downloads in the last 30 days: 36 - Likes: 33 - License: apache-2.0 - Languages: en - Based on: Dream-org/Dream-VLA-7B - Tags: pytorch, safetensors, vision-language-action, robotics, robosuite, lora, flow-matching, diffusion-language-model, simulation ## Links - Model: https://huggingface.co/RobotLearningLab/Jocasta-diff-VLA - Files: https://huggingface.co/RobotLearningLab/Jocasta-diff-VLA/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download RobotLearningLab/Jocasta-diff-VLA ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 7 KB, downloaded as a zip. ```text README.md 7 KB ``` ## Source [RobotLearningLab/Jocasta-diff-VLA on Hugging Face](https://huggingface.co/RobotLearningLab/Jocasta-diff-VLA) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Jocasta-diff-VLA (this page) | 36 | 33 | — | apache-2.0 | 2026-11-06 | | [Alpamayo 2 Edge](https://mindket.com/p/ee30e2b4-7cdf-4933-b8a0-f9b993f16dd0) | 921 | 41 | 2.7B | openmdw-1.1 | 2026-11-10 | | [π₀.₅ (Pi05) (LeRobot)](https://mindket.com/p/5d6d125f-e079-443d-9e49-b579ca0d2ae1) | 21,996 | 119 | 3.6B | gemma | 2026-10-06 | | [K2-Horizon-7B-Uno](https://mindket.com/p/4f8072eb-e9bd-4294-8bcf-f9303cdd68a3) | 203 | 113 | — | apache-2.0 | 2026-09-05 | | [Iris-3B by speridlabs](https://mindket.com/p/0abd1bcc-dae6-497c-b706-d6bc43cde2df) | 997 | 350 | 3B | apache-2.0 | 2026-10-12 | | [Bielik-11B-v3.1-DFlash2](https://mindket.com/p/2711a318-550d-40de-b3db-4e250b26a909) | 44 | 3 | 1.2B | apache-2.0 | 2026-11-11 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-06 - First published: 2026-11-03 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open RobotLearningLab/Jocasta-diff-VLA on Hugging Face](https://huggingface.co/RobotLearningLab/Jocasta-diff-VLA) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/eb993a40-037d-4560-917d-fc85a9c3faa9) - [Its page on Mindket](https://mindket.com/p/eb993a40-037d-4560-917d-fc85a9c3faa9)