--- license: other license_name: rlwrld-model-license-v1.0 license_link: LICENSE.md library_name: transformers pipeline_tag: robotics tags: - robotics - vla - vision-language-action - manipulation - flow-matching - rldx base_model: Qwen/Qwen3-VL-8B-Instruct --- # RLDX-1 [Paper](https://arxiv.org/abs/2605.03269)  ·  [Project page](https://rlwrld.ai/rldx-1)  ·  [Code](https://github.com/RLWRLD/RLDX-1)  ·  [Models](https://huggingface.co/collections/RLWRLD/rldx-1)

RLDX-1 teaser

**RLDX-1** is a general-purpose Robot Foundation Model designed for dexterous manipulation. Powered by a **Multi-Stream Action Transformer (MSAT)**, it seamlessly unifies multimodal perception (visual + tactile), high-DoF actuation, and memory-aware decision-making in a single architecture. RLDX-1 achieves state-of-the-art performance across diverse simulation benchmarks and is fully validated on real-world hardware. This repository hosts **`RLDX-1-PT`** — a foundation checkpoint pretrained on a broad mixture of public manipulation corpora, from which all downstream `RLDX-1-{FT,MT}-*` releases finetune. Use it as your starting point for new embodiments and tasks.

RLDX-1 architecture

## Highlights - **Multi-Stream Action Transformer (MSAT).** Cognition, physics, and action each get a dedicated stream coupled by joint self-attention — an extension of MM-DiT to action modeling. - **Motion awareness.** Multi-frame observations + a motion module capture temporal dynamics; intermediate VLM layers compress video tokens to keep the policy efficient. - **Long-term memory.** A memory module fuses past cognition features with the current ones for history-grounded decisions beyond a short multi-frame window. - **Physical sensing.** Tactile and torque enter as a dedicated physics stream; the decoder is jointly trained to predict future physical signals. - **Three-stage training.** Pre-training (generalization) → mid-training (functionality) → post-training (task adaptation), with synthetic data augmenting rare manipulation scenarios. - **Real-time inference.** Static graph capture + custom fused kernels bring the all-modality model to **43.7 ms / step on RTX 5090 (1.63× speedup, >22 Hz)**. ## Released Checkpoints This card describes `RLDX-1-PT` (foundation). The full RLDX-1 model family: | Checkpoint | Description | Params | Embodiment Tag | |---|---|---|---| | [`RLDX-1-PT`](https://huggingface.co/RLWRLD/RLDX-1-PT) | Multi-source pretrained foundation (this repo) | 6.9B | per-dataset | | [`RLDX-1-VLM`](https://huggingface.co/RLWRLD/RLDX-1-VLM) | Qwen3-VL-8B vision-language backbone | 8B | — | | [`RLDX-1-FT-ROBOCASA`](https://huggingface.co/RLWRLD/RLDX-1-FT-ROBOCASA) | RoboCasa Kitchen 24-task finetune | 6.9B | `GENERAL_EMBODIMENT` | | [`RLDX-1-FT-RC365`](https://huggingface.co/RLWRLD/RLDX-1-FT-RC365) | RoboCasa-365 cross-task finetune | 6.9B | `GENERAL_EMBODIMENT` | | [`RLDX-1-FT-LIBERO`](https://huggingface.co/RLWRLD/RLDX-1-FT-LIBERO) | LIBERO 4-task suite (goal, object, spatial, long) finetune | 6.9B | `GENERAL_EMBODIMENT` | | [`RLDX-1-FT-SIMPLER-GOOGLE`](https://huggingface.co/RLWRLD/RLDX-1-FT-SIMPLER-GOOGLE) | SIMPLER Google VM/VA finetune | 6.9B | `OXE_FRACTAL` | | [`RLDX-1-FT-SIMPLER-WIDOWX`](https://huggingface.co/RLWRLD/RLDX-1-FT-SIMPLER-WIDOWX) | SIMPLER WidowX finetune | 6.9B | `OXE_BRIDGE_ORIG` | | [`RLDX-1-FT-GR1`](https://huggingface.co/RLWRLD/RLDX-1-FT-GR1) | GR-1 Tabletop finetune | 6.9B | `GENERAL_EMBODIMENT` | | [`RLDX-1-MT-DROID`](https://huggingface.co/RLWRLD/RLDX-1-MT-DROID) | DROID mid-train | 8.1B | `OXE_DROID` | | [`RLDX-1-MT-ALLEX`](https://huggingface.co/RLWRLD/RLDX-1-MT-ALLEX) | All add-ons (memory + motion + physics + video) | 8.1B | `GENERAL_EMBODIMENT` | ## Performance Success rate (%) of RLDX-1 finetuned on each benchmark's training set, evaluated with the linked checkpoint. | Benchmark | Success Rate | Checkpoint | |---|---|---| | LIBERO (Avg) | 97.8 | `RLDX-1-FT-LIBERO` | | LIBERO-Plus | 87.6 | `RLDX-1-FT-LIBERO` | | SIMPLER Google-VM | 81.5 | `RLDX-1-FT-SIMPLER-GOOGLE` | | SIMPLER Google-VA | 77.4 | `RLDX-1-FT-SIMPLER-GOOGLE` | | SIMPLER WidowX | 71.9 | `RLDX-1-FT-SIMPLER-WIDOWX` | | RoboCasa Kitchen (24 tasks) | 70.6 | `RLDX-1-FT-ROBOCASA` | | GR-1 Tabletop | 58.7 | `RLDX-1-FT-GR1` | | RoboCasa365 (Avg) | 31.5 | `RLDX-1-FT-RC365` | ## Quick start ```bash git clone https://github.com/RLWRLD/RLDX-1.git cd RLDX uv sync --python 3.10 uv pip install -e . ``` ### Inference (single step) ```python from rldx.policy.rldx_policy import RLDXPolicy from rldx.data.embodiment_tags import EmbodimentTag policy = RLDXPolicy( model_path="RLWRLD/RLDX-1-FT-ROBOCASA", embodiment_tag=EmbodimentTag.GENERAL_EMBODIMENT, device="cuda:0", ) action = policy.get_action(observation) ``` `RLDX-1-PT` is pretrained on a multi-source mixture, so for direct inference pair it with the embodiment tag matching your data source — e.g. `OXE_FRACTAL`, `OXE_BRIDGE_ORIG`, `OXE_DROID`, `GALAXEA`, `AGIBOT_GRIPPER`, `AGIBOT_DEXHAND`, `NEURAL_GR1`, `HUMANOID_EVERYDAY_G1`, `HUMANOID_EVERYDAY_H1`, etc. For custom robots, finetune. ### Real-time serving (ZeroMQ) ```bash uv run python rldx/eval/run_rldx_server.py \ --model-path RLWRLD/RLDX-1-FT-ROBOCASA \ --embodiment-tag GENERAL_EMBODIMENT \ --host 0.0.0.0 --port 20000 ``` A WebSocket server (`run_rldx_server_pi.py`) is also available for openpi-compatible clients. ### Finetune from `RLDX-1-PT` ```bash uv run python rldx/experiment/launch_train.py \ --base-model-path RLWRLD/RLDX-1-PT \ --dataset-path /path/to/your/dataset \ --embodiment-tag GENERAL_EMBODIMENT \ --video-length 4 --n-cog-tokens 64 \ --global-batch-size 64 --learning-rate 1e-4 \ --max-steps 60000 --save-steps 5000 \ --output-dir ./outputs/my_finetune ``` To enable add-ons (memory / motion / physics) see the recipes in the [main README](https://github.com/RLWRLD/RLDX-1#finetuning) and the [`training.md`](https://github.com/RLWRLD/RLDX-1/blob/main/docs/training.md) guide. ## Model details - **Architecture:** Multi-Stream Action Transformer (MSAT) policy with a Qwen3-VL vision-language backbone, cognition-token perceptual summary, optional Transformer memory, motion module, and tactile/torque physics encoder/decoder. Trained with flow matching. - **Inputs:** RGB video (default 4 frames), state proprioception, optional tactile / torque signals, language instruction. - **Outputs:** Action chunks of length 16 (default `--action-horizon 16`). - **Backbone:** [`Qwen/Qwen3-VL-8B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct). - **Pretraining data:** A mixture of public manipulation corpora, covering 27 [Open X-Embodiment (OXE)](https://robotics-transformer-x.github.io/) datasets (DROID, Bridge, Fractal, Language Table, …) plus [Galaxea](https://galaxea.ai/), [AgiBot World](https://agibot-world.com/) (Gripper + Dexhand), ActionNet, Neural-Curated GR-1 humanoid trajectories, and Unitree G1 / H1 from [HumanoidEveryday](https://lipeng-zhou.github.io/HumanoidEveryday/). For a full architectural walkthrough see [`docs/architecture.md`](https://github.com/RLWRLD/RLDX-1/blob/main/docs/architecture.md). ## Intended use & limitations **Intended use.** Research on robotic manipulation, finetuning on custom embodiments, simulation benchmarking, and non-commercial real-robot deployment under the conditions of the RLWRLD Model License v1.0. **Out of scope.** Commercial deployment, military or weapons applications, non-consensual surveillance, and any use that violates applicable laws or regulations. See [`LICENSE.md`](LICENSE.md) §3.5 for the full list. **Limitations.** Performance depends heavily on embodiment match and data distribution. The pretrained checkpoint is OXE-conditioned and is not guaranteed to work zero-shot on novel embodiments without finetuning. Memory, motion, and physics modules are dormant in `RLDX-1-PT` and only activate when the corresponding flags are wired during finetuning (see `RLDX-1-MT-ALLEX`). ## Citation ```bibtex @article{rldx2026, title={RLDX-1 Technical Report}, author={Kim, Dongyoung and Jang, Huiwon and Koo, Myungkyu and Jang, Suhyeok and Kim, Taeyoung and others}, year={2026}, note={RLWRLD}, eprint={2605.03269}, archivePrefix={arXiv}, url={https://arxiv.org/abs/2605.03269} } ``` ## License Released under the **RLWRLD Model License v1.0** — a non-commercial license with attribution and share-alike requirements. See [`LICENSE.md`](LICENSE.md) for the full text. By using this model you agree to those terms, including the use restrictions in §3.5.