File size: 6,497 Bytes
587e9ec
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
---
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
  - robocasa
base_model: RLWRLD/RLDX-1-PT
---

# RLDX-1-FT-RC365

[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)

<p align="center">
<img src="teaser.png" width="100%" alt="RLDX-1 teaser">
</p>

**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.

This repository hosts **`RLDX-1-FT-RC365`** — RLDX-1 finetuned on the
**RoboCasa-365** cross-task generalization suite. It achieves **31.5%**
average success across the 365 tasks, which span a much broader scene and
skill distribution than the standard RoboCasa Kitchen suite.

## 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)**.

## Performance

| Benchmark | Success Rate |
|---|---|
| RoboCasa-365 (365-task avg) | **31.5%** |

## Quick start

### Installation

```bash
git clone https://github.com/RLWRLD/RLDX-1.git
cd RLDX
uv sync --python 3.10
uv pip install -e .
```

### Inference

```python
from rldx.policy.rldx_policy import RLDXPolicy
from rldx.data.embodiment_tags import EmbodimentTag

policy = RLDXPolicy(
    model_path="RLWRLD/RLDX-1-FT-RC365",
    embodiment_tag=EmbodimentTag.GENERAL_EMBODIMENT,
    device="cuda:0",
)

action = policy.get_action(observation)
```

### Real-time serving (ZeroMQ)

```bash
uv run python rldx/eval/run_rldx_server.py \
    --model-path RLWRLD/RLDX-1-FT-RC365 \
    --embodiment-tag GENERAL_EMBODIMENT \
    --host 0.0.0.0 --port 20000
```

To reproduce the benchmark numbers end-to-end, see
[`run_scripts/eval/robocasa_365/README.md`](https://github.com/RLWRLD/RLDX-1/blob/main/run_scripts/eval/robocasa_365/README.md).

## Model details

- **Architecture:** Multi-Stream Action Transformer (MSAT) policy on a
  Qwen3-VL backbone with cognition-token perceptual summary. Trained with
  flow matching.
- **Inputs:** RGB video (default 4 frames), state proprioception, language
  instruction.
- **Outputs:** Action chunks of length 16.
- **Embodiment tag:** `GENERAL_EMBODIMENT`.
- **Base model:** [`RLWRLD/RLDX-1-PT`](https://huggingface.co/RLWRLD/RLDX-1-PT).
- **Backbone:** [`Qwen/Qwen3-VL-8B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct).
- **Finetune data:** RoboCasa-365 (365 tasks).
- **Params:** 6.9B.

For the full architectural walkthrough see
[`docs/architecture.md`](https://github.com/RLWRLD/RLDX-1/blob/main/docs/architecture.md).

## RLDX-1 model family

| Checkpoint | Description |
|---|---|
| [`RLDX-1-PT`](https://huggingface.co/RLWRLD/RLDX-1-PT) | Multi-source pretrained foundation |
| [`RLDX-1-VLM`](https://huggingface.co/RLWRLD/RLDX-1-VLM) | Qwen3-VL-8B vision-language backbone |
| [`RLDX-1-FT-ROBOCASA`](https://huggingface.co/RLWRLD/RLDX-1-FT-ROBOCASA) | RoboCasa Kitchen 24-task finetune |
| [`RLDX-1-FT-RC365`](https://huggingface.co/RLWRLD/RLDX-1-FT-RC365) | RoboCasa-365 cross-task finetune (this repo) |
| [`RLDX-1-FT-LIBERO`](https://huggingface.co/RLWRLD/RLDX-1-FT-LIBERO) | LIBERO 4-task suite (goal, object, spatial, long) finetune |
| [`RLDX-1-FT-SIMPLER-GOOGLE`](https://huggingface.co/RLWRLD/RLDX-1-FT-SIMPLER-GOOGLE) | SIMPLER Google VM/VA finetune |
| [`RLDX-1-FT-SIMPLER-WIDOWX`](https://huggingface.co/RLWRLD/RLDX-1-FT-SIMPLER-WIDOWX) | SIMPLER WidowX finetune |
| [`RLDX-1-FT-GR1`](https://huggingface.co/RLWRLD/RLDX-1-FT-GR1) | GR-1 Tabletop finetune |
| [`RLDX-1-MT-DROID`](https://huggingface.co/RLWRLD/RLDX-1-MT-DROID) | DROID mid-train |
| [`RLDX-1-MT-ALLEX`](https://huggingface.co/RLWRLD/RLDX-1-MT-ALLEX) | All add-ons (memory + motion + physics + video) |

## Intended use & limitations

**Intended use.** Research on robotic manipulation, generalization studies
on the RoboCasa-365 suite, 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.** RoboCasa-365 deliberately probes broad task generalization,
so absolute success rate is lower than focused 24-task RoboCasa Kitchen
finetuning. For RoboCasa Kitchen specifically, prefer
[`RLDX-1-FT-ROBOCASA`](https://huggingface.co/RLWRLD/RLDX-1-FT-ROBOCASA).
For other embodiments or datasets, finetune from
[`RLDX-1-PT`](https://huggingface.co/RLWRLD/RLDX-1-PT) instead.

## 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.