RT-1
Google, US · December 2022
Robotics at Google, Everyday Robots and Google Research
- Parameters
- 35 M
- The paper states 35M total parameters.
- GPU memory
- 0.1 GB
- weights at bf16, computed. Fits an 8 GB card
- Inference latency
- not published
- per action step
- Weights
- Apache-2.0
- downloadable checkpoint
What it is
RT-1 is the original Robotics Transformer, a 35M parameter language-conditioned imitation policy that maps camera images and a task string to discretized action tokens. It has no vision-language backbone, which is exactly why it fits on the robot and runs closed loop at 3 Hz. Its evidence base is real hardware only, 130k episodes across more than 700 tasks gathered with 13 robots over 17 months. It is still the cheapest historically important checkpoint to run, but the 3 Hz rate and the absence of web-scale semantics put it well behind current VLAs on anything requiring open-vocabulary understanding.
Architecture
- Backbone
- No VLM backbone. FiLM conditioned EfficientNet image tokenizer, TokenLearner compression to 8 tokens per image, and a decoder-only Transformer with 8 self-attention layers.
- Action head
- Discretized action tokens over 11 dimensions (7 for the arm: x, y, z, roll, pitch, yaw, gripper opening; 3 for the mobile base: x, y, yaw; plus a mode switch).
- Control rate
- 3 Hz
- Parameters
- The paper states 35M total parameters. The decoder-only Transformer accounts for 19M of these, the rest sits in the FiLM EfficientNet tokenizer. Only one model size was released.
- Pretraining data
- Over 130k real robot demonstration episodes covering over 700 tasks, collected with a fleet of 13 robots over 17 months. A bin-picking dataset from a Kuka arm was added for the multi-robot transfer study.
- Embodiments
- Everyday Robots mobile manipulator (7 DoF arm plus mobile base), Kuka arm (bin-picking data used only in the data-mixing transfer experiment)
What hardware it needs
This is the section most people came for, so it is worth being precise about which numbers are measured and which are arithmetic.
| Precision | Weights | Note |
|---|---|---|
| fp32 | 0.1 GB | Training master weights, rarely used for inference |
| bf16 or fp16 | 0.1 GB | The usual way these checkpoints are served |
| int8 | 0.0 GB | Quantised, expect some loss of precision on fine motions |
| int4 | 0.0 GB | Aggressive quantisation, verify success rate before trusting it |
Smallest real card that fits the bf16 weights plus a 40 percent runtime allowance: 8 GB. That is an estimate for inference, not for fine tuning. Full fine tuning also holds optimiser state and gradients, which typically costs several times the weights unless you use LoRA or a comparable adapter.
| Control rate | 3 Hz |
Reported results
Grouped by what the evaluation actually asked. Values from different suites are not comparable with each other, so each one keeps its suite and split.
Simulation
No results in this category are published for this model.
Real world
Rollouts on physical hardware. The setups differ, so read these as evidence, not as a ranking.
- RT-1 real-robot evaluation seen tasks, over 700 instructions97%success rateAuthors' own evaluation. Reported as 25 points above BC-Z and 32 points above Gato.source
- RT-1 real-robot evaluation distractor robustness83%success ratesource
- RT-1 real-robot evaluation unseen instructions76%
- SayCan long-horizon kitchen Kitchen1, up to 50 steps67%execution success ratesource
- RT-1 real-robot evaluation background robustness59%success rateThe weakest of the four robustness categories, which is where RT-1 degrades first.source
Fine tuned tasks
No results in this category are published for this model.
On real hardware
Trained and evaluated entirely on real hardware. 13 robots, 17 months of data collection, over 700 instructions at 97 percent success on seen tasks and 76 percent on unseen instructions. Long-horizon kitchen sequences of up to 50 steps reached 67 percent execution success in Kitchen1.
Fine tuning it yourself
The released repository contains three SavedModel checkpoints (RT-1 trained on 700 tasks, RT-1 multi-robot on EDR plus Kuka, and RT-1 sim-real). The README covers loading and inference, not a supported fine-tuning pipeline, so adapting it to a new robot means rewriting the data pipeline to the RT-1 action discretization.
Where it helps, where it does not
Strengths
- Genuinely small at 35M parameters, so it runs closed loop at 3 Hz without cloud inference
- Weights and code are Apache-2.0 and the checkpoints ship inside the repository
- The largest real-robot evidence base of its generation (130k episodes, 700 tasks, 17 months)
- Documented positive transfer when mixing data from a second robot type (22 percent to 39 percent)
Limits
- 3 Hz control is too slow for contact-rich or reactive manipulation
- No vision-language backbone, so semantic generalization is far below later VLAs
- Background robustness drops to 59 percent, the clearest failure mode
- Single-arm plus mobile base only, and the hardware (Everyday Robots) no longer exists
- The GitHub repository was archived on 7 April 2025 and is read-only, and the code is TensorFlow-era
Sources
Everything on this page was taken from these documents. Where they disagree with what you read here, they win.
- https://arxiv.org/abs/2212.06817
- https://robotics-transformer1.github.io/
- https://github.com/google-research/robotics_transformer
- https://ar5iv.labs.arxiv.org/html/2212.06817
Entry last checked 2026-08-11.
Try a policy on a real arm
A physical SO-100 is online and free to drive in the browser, and five of the models in this arena can be fine tuned on a dataset you record with your own.