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Qwen-RobotManip is the closest direct open-source alternative to Kyutai REKI identified in the available primary-source material. It is explicitly presented as a generalizable vision-language-action (VLA) foundation model for manipulation. OpenVLA, SmolVLA, Octo and RDT-1B are also important open model families, but the right choice depends less on a universal leaderboard than on your robot embodiment, action interface, data pipeline, deployment hardware and evaluation protocol.
REKI itself is not a single robot-agnostic checkpoint. Kyutai publishes separate variants for Franka Panda, dual-arm ALOHA, PandaOmron mobile manipulation and the Fourier GR-1 humanoid platform. A model that performs well on one of those setups cannot be assumed to transfer unchanged to another arm, gripper or control stack.
What Kyutai REKI is
Kyutai describes REKI as a 2.4-billion-parameter vision-language-action model pretrained exclusively on public data. Its stated goal is to improve the performance-to-size trade-off: the project calls it a new Pareto frontier between model size and performance. The October 2026 release includes pretrained and fine-tuned weights, code for data curation, training, inference, evaluation and deployment, and robot-specific demonstrations.
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Architecturally, REKI combines a vision-language model (VLM) with a flow-matching action expert. The VLM interprets camera observations, language and other inputs; the action expert turns that context into a sequence of robot actions. A register-token memory stores summaries of recent VLM outputs in the action expert cache. Kyutai reports that this temporal memory reduces oscillatory behavior in its evaluations, an important consideration for manipulation tasks in which a controller can otherwise overcorrect from frame to frame.
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The pretraining mixture includes public robot datasets such as RoboSet, Galaxea, AgiBot World, RoboCOIN, RoboMIND, RDT-1B, DROID, BridgeData V2, RT-1 and BC-Z. It also includes simulated motion-planned trajectories, egocentric datasets including EgoDex, EgoVerse and Xperience, and selected vision-language data. Kyutai reports a pretraining run of 200,000 steps on 32 H100 GPUs. Those figures describe the reported training run, not the hardware you necessarily need for inference or fine-tuning.
At-a-glance comparison
| Model or family | What the available primary material establishes | Useful distinction from REKI | Details you must verify before adoption |
|---|---|---|---|
| Qwen-RobotManip | Official README calls it a generalizable VLA foundation model built on Qwen-VL / Qwen3.5-4B. It reports approximately 38,100 hours of pretraining data from open robotic-manipulation datasets and egocentric human videos. | The clearest direct alternative because manipulation generalization is its stated purpose. | Current license, checkpoint access, supported embodiments, inference requirements and benchmark protocol. |
| OpenVLA | Listed by the Robot Foundation Layer (RFL) as an open VLA foundation model targeted for interoperability. | A mature open family to investigate when ecosystem compatibility matters. | Exact checkpoint, license, robot support, action representation and results under your task protocol. |
| SmolVLA | Listed by RFL as an interoperability target. | Potentially attractive when a smaller, more accessible model is a priority. | Current parameter count, latency, hardware needs, license and benchmark results relative to REKI. |
| Octo | Listed by RFL as an open robot-policy family. | Relevant where you value an established generalist-policy ecosystem. | Embodiment adapters, action interface, current checkpoints and comparable manipulation scores. |
| RDT-1B | Named by RFL and included in REKI’s public-data mixture. | Useful for lineage and dataset-compatibility analysis. | Do not infer equal architecture, transfer or performance merely from shared datasets. |
The retrieved sources do not provide a common, same-protocol benchmark table covering every model above. Therefore, no responsible comparison can declare one universal winner. A result is meaningful only when the robot, demonstrations, camera placement, action convention, randomization and success definition are held constant.
Qwen-RobotManip: the closest direct alternative
Qwen-RobotManip is the strongest first candidate when the question is, “What should I try instead of REKI for general manipulation?” Its official README describes it as a generalizable VLA foundation model built upon Qwen-VL / Qwen3.5-4B. The project reports an approximately 38,100-hour pretraining corpus assembled from open robotic-manipulation datasets and egocentric human videos.
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When Qwen-RobotManip is a sensible first test
- You want a manipulation-focused foundation model rather than a policy tied to one benchmark.
- Your team can adapt a Qwen-based vision-language stack to the robot’s observation and action interfaces.
- You are prepared to validate license terms and checkpoint availability before collecting deployment data.
- You can reproduce the model’s evaluation setup instead of comparing a result from one embodiment with a result from another.
What it does not prove
The reported 38,100-hour corpus does not establish that Qwen-RobotManip outperforms REKI. It also does not establish support for Franka, ALOHA, PandaOmron or GR-1 without a matching checkpoint or adapter. Shared use of public datasets is evidence of possible data compatibility, not evidence of identical architecture, action heads or transfer behavior.
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OpenVLA
OpenVLA is named by the Robot Foundation Layer as one of the open VLA foundation models it targets for interoperability. That makes it worth investigating when you need an established open-policy ecosystem, existing adapters or community tooling. The available material does not supply a same-protocol comparison with REKI, so descriptions such as “faster,” “more accurate” or “better on manipulation” would be unsupported here.
For an OpenVLA evaluation, identify the exact checkpoint and model card, then record its license, required image and proprioception fields, action dimensionality, action chunking and control frequency. Verify whether your robot is represented natively or requires a retargeting layer. A model can be open while its demonstrations, preprocessing scripts or deployment wrapper remain incomplete for your particular arm.
SmolVLA
SmolVLA is also listed by the Robot Foundation Layer as an interoperability target. Its likely appeal is accessibility: a smaller model class may fit a constrained inference computer or allow more rapid local experimentation. However, the available primary result does not establish a current parameter count, latency, license or benchmark result relative to REKI.
Treat “small” as a deployment hypothesis, not a specification. Measure end-to-end latency, including image capture, preprocessing, model inference, action decoding and robot communication. A compact policy that misses the control deadline can be less useful than a larger policy running on a stronger accelerator. Confirm memory use and action rate on the exact hardware you plan to install.
Octo
Octo appears in the RFL model list as an open robot-policy family. It is a reasonable candidate for teams that value a generalist-policy ecosystem and want to compare against a model with existing research adoption. The available evidence does not justify claiming that Octo beats or matches REKI on any particular manipulation benchmark.
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Octo should be evaluated by interface fit. Check how it represents observations, language instructions, robot state and actions; then determine whether a native embodiment configuration exists. If you need to write an adapter, keep the adapter separate from the policy so that normalization, gripper semantics and coordinate transforms can be audited independently.
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RDT-1B is relevant in two ways: RFL lists it as an open model family, and REKI lists RDT-1B among the public robot datasets in its pretraining mixture. That shared appearance makes RDT-1B useful for studying data lineage and compatibility, but it does not imply that the two systems share an architecture or will transfer equally to your hardware.
Use RDT-1B as a comparison point when your data pipeline already contains trajectories in a compatible format. Confirm the original action coordinate system, temporal resolution, gripper encoding and camera conventions before mixing demonstrations. Converting trajectories without preserving those semantics can create a dataset that looks large but teaches inconsistent behavior.
Match the model to the embodiment
REKI’s published variants show why embodiment is the first decision, not an afterthought. The project identifies these combinations:
| REKI variant | Robot or setup | Reported evaluation context | Implication for alternative models |
|---|---|---|---|
| Franka Panda | Single-arm Panda | LIBERO | Check seven-degree-of-freedom arm state, gripper representation and the exact LIBERO task configuration. |
| ALOHA | Dual-arm ALOHA | RoboTwin | Confirm whether a candidate outputs synchronized actions for both arms and uses the same camera layout. |
| PandaOmron | Mobile-manipulation platform | RoboCasa365 | Account for base motion, navigation state and manipulation timing; a fixed-arm checkpoint is not automatically compatible. |
| Fourier GR-1 | Humanoid platform | Project-specific variant | Verify whole-body or upper-body action definitions, joint limits and safety interlocks before attempting transfer. |
These pairings are reported REKI variants, not a promise that every alternative supports the same robots. For any candidate, require a documented mapping for joint or Cartesian actions, gripper commands, observation timestamps and reset behavior.
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Interface checks before you download a checkpoint
A model name is not an integration specification. Record the following fields in a one-page interface contract before writing an adapter:
- Camera inputs: camera names, count, mounting positions, image resolution, color format and frame rate.
- Proprioception: joint positions, velocities, end-effector pose, gripper state and timestamp conventions.
- Action space: joint, Cartesian or delta commands; rotation representation; gripper units; dimensionality; and valid ranges.
- Temporal behavior: control frequency, action horizon, chunk length, interpolation and whether actions are replanned asynchronously.
- Coordinate frames: base, tool, camera and world frames, including handedness and axis order.
- Normalization: training means, standard deviations, clipping limits and any dataset-specific scaling.
- Language input: instruction format, maximum length, tokenization and whether language is required at every control cycle.
REKI documents a LeRobot asynchronous-inference gRPC policy server for real-robot deployment. That is an operational pattern rather than a guarantee that an alternative can use the same server unchanged. If you adopt the pattern, separate the robot process, observation publisher, inference service and action executor so that a stalled model request cannot bypass the emergency-stop path.
Data openness and reproducibility
Compare projects on more than whether weights can be downloaded. A reproducible manipulation policy needs public or clearly licensed data, preprocessing scripts, training configuration, evaluation code and an explanation of native control conventions.
REKI states that its pretraining uses public data only and releases tooling for curation. Its reported 200,000-step run on 32 H100 GPUs gives a scale reference for pretraining, but it does not mean a user must reproduce that run to fine-tune a checkpoint. Ask instead whether the project publishes a practical fine-tuning recipe, expected batch sizes, optimizer settings, image resolution and hardware requirements.
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A practical evaluation workflow
- Freeze the task definition. Choose a fixed set of manipulation tasks, success criteria, maximum episode length and reset procedure. Write down whether success requires object pose, grasp stability, placement tolerance or a human judgment.
- Calibrate the robot and cameras. Verify intrinsic and extrinsic calibration, joint limits, gripper homing and timestamp synchronization. Save the calibration files with the experiment identifier.
- Build an observation adapter. Convert camera frames and proprioceptive state into the candidate’s expected names, shapes, units and ordering. Log both raw and converted observations.
- Build an action adapter. Convert model outputs into the robot controller’s command format, apply documented clipping and rate limits, and test with motors disabled or the arm mechanically restrained.
- Measure closed-loop latency. Record capture, preprocessing, inference, decoding, transport and actuation times separately. Test worst-case latency, not only the average.
- Run scripted safety tests. Verify emergency stop, workspace bounds, collision behavior, communication loss handling and recovery after a dropped frame or delayed inference response.
- Evaluate without changing variables. Use the same seeds, camera placement, demonstrations, task order and success definition for every model. Report failures as well as successes.
- Repeat on held-out conditions. Change object appearance, lighting, placement and language wording only after the baseline is stable. Keep those changes identical across candidates.
Safety and operations for real hardware
No model card can establish safe behavior on your robot. Before allowing autonomous motion, implement independent limits for joint position, velocity, torque or force where available, Cartesian workspace and gripper pressure. Keep a physical emergency stop within reach and test it while the policy is moving.
- Use a low-speed, low-force commissioning mode for the first runs.
- Require a human to approve the initial action chunk after every reset.
- Stop on stale observations, missing camera frames, invalid numbers, out-of-range actions or a heartbeat timeout.
- Log every observation timestamp, model response, command, limit intervention and stop reason.
- Provide a deterministic recovery pose that does not require another model inference.
- Test communication loss by disconnecting the inference service under controlled conditions.
Oscillation deserves special attention in visual control. REKI’s register-token memory is reported to reduce oscillatory behavior in its evaluations, but that result should not be generalized to another embodiment or controller. Measure oscillation on your own arm using position error, command reversals and settling time, and add a conventional rate limiter or hold-last-safe-command policy where appropriate.
Which model should you choose?
| Your priority | First candidate to investigate | Reason | Decision gate |
|---|---|---|---|
| Closest conceptual substitute for REKI | Qwen-RobotManip | Explicitly positioned as a generalizable manipulation VLA with a reported 38,100-hour corpus. | License, checkpoint and embodiment support must be confirmed. |
| Interoperability and existing open-policy tooling | OpenVLA | Named by RFL as an interoperability target. | Match its action and observation contract to your robot. |
| Constrained inference hardware | SmolVLA | Potentially smaller and easier to deploy. | Measure actual latency and memory; do not rely on the name. |
| Generalist-policy ecosystem | Octo | Open family listed by RFL for interoperability. | Verify current checkpoints and manipulation results. |
| Data-lineage comparison with REKI | RDT-1B | Listed by RFL and present in REKI’s public-data mixture. | Confirm that action conventions and embodiment assumptions align. |
| Robot already covered by a published REKI variant | REKI itself | Published variants cover Franka Panda, ALOHA, PandaOmron and Fourier GR-1 contexts. | Use the matching variant and reproduce its interface before comparing alternatives. |
How to make a fair REKI-versus-alternative comparison
Publish the robot model, gripper, camera placement, image resolution, proprioceptive fields, action representation, control frequency, action chunking, inference hardware, software commit, checkpoint hash, fine-tuning data and success definition. State whether each result is zero-shot, prompted, fine-tuned or adapted with demonstrations.
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Do not combine a REKI result from LIBERO with an OpenVLA result from a different arm and call the larger number a model ranking. If two systems cannot run under the same embodiment and task protocol, present them as engineering options rather than as directly comparable scores. This discipline is especially important for humanoids and mobile manipulators, where base motion and whole-body coordination change the problem substantially.
Bottom line
Start with Qwen-RobotManip if you need the nearest stated alternative to REKI for general robotic manipulation. Investigate OpenVLA, SmolVLA, Octo and RDT-1B when interoperability, model size, ecosystem or data lineage matters more than a direct one-to-one replacement. Keep REKI in the comparison whenever its published embodiment variants match your robot. The final choice should come from a controlled, safety-gated evaluation on your hardware, not from parameter count, dataset hours or a benchmark borrowed from another robot.
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