# reBot Arm

> Source: https://aiwiki.ai/wiki/rebot_arm
> Updated: 2026-09-02
> Fact-checked: 2026-09-02
> Categories: AI Hardware, Embodied AI, Robot Hardware, Robotics
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) - attribute to "AI Wiki (aiwiki.ai)"
> Cite as: AI Wiki. "reBot Arm." aiwiki.ai, 2 Sept 2026. https://aiwiki.ai/wiki/rebot_arm
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution.

**reBot Arm** is an open-hardware desktop robotic-arm series developed by Seeed Studio for robotics education, teleoperation, and embodied-AI experiments. The series has two main builds, reBot Arm B601-DM and reBot Arm B601-RS, which share a six-axis arm and one actuated gripper but use different motor families and electrical systems. Seeed publishes mechanical source files, bills of materials, control software, and integration guides for both builds.[1][2]

The project is positioned as a development platform rather than a validated industrial robot. Its public materials document [LeRobot](https://aiwiki.ai/wiki/lerobot), ROS, MoveIt, Pinocchio, [NVIDIA Isaac Sim](https://aiwiki.ai/wiki/nvidia_isaac_sim), and [Isaac GR00T](https://aiwiki.ai/wiki/isaac_gr00t) workflows. They do not provide an independent safety certification, service-life study, or controlled reliability benchmark for the arm.[1][3]

## Project history and licensing

Seeed created the public `Seeed-Projects/reBot-DevArm` repository in November 2025. The repository says the project initially used a noncommercial Creative Commons license, then changed its hardware license to CERN Open Hardware Licence Version 2, Weakly Reciprocal, on May 11, 2026. Software in the project is licensed under Apache License 2.0.[1]

The Open Source Hardware Association certified reBot Arm B601 as open-source hardware on May 14, 2026, under identifier CN000024. The certification record covers the B601-DM and B601-RS, lists CERN-OHL-W-2.0 for hardware and documentation, and lists Apache for software.[2]

The released hardware material includes STEP files for printed, machined, and purchased parts, along with bills of materials. Seeed cautions that the published B601-RS bill of materials does not necessarily represent the final shipping version. The open files therefore document a buildable design, but they should not be treated as an exact manufacturing record for every retail unit.[4]

## Hardware variants

Both B601 variants have six arm joints and an actuated gripper. The B601-DM uses Damiao motors and a 24 V supply. The B601-RS uses RobStride motors and a 48 V supply. Seeed's repository gives the following series-level specifications.[1]

| Specification | B601-DM | B601-RS |
| --- | --- | --- |
| Actuated axes | 6 arm joints plus 1 gripper | 6 arm joints plus 1 gripper |
| Motor family | Damiao DM series | RobStride RS series |
| Rated payload | 1.5 kg | 2.5 kg |
| Maximum reach | 767 mm | 754 mm |
| Supply voltage | 24 V DC | 48 V DC |
| Listed software ecosystems | ROS 1, ROS 2, LeRobot, Pinocchio, Isaac Sim, Python SDK | ROS 1, ROS 2, LeRobot, Pinocchio, Isaac Sim, Python SDK |

Current B601-RS documentation describes three RobStride RS06 motors on the shoulder and elbow joints and four RS00 motors across the wrist and gripper. The motors communicate over a 1 Mbps CAN bus. The same documentation lists a 2.5 kg rated payload, 5 kg maximum load, 754.7 mm reach including the gripper, and 587.5 mm reach without it.[5] The 5 kg figure is a vendor-stated maximum rather than a rated continuous payload.

Seeed's first-party pages disagree on two B601-RS measurements. The September 2026 course and the B601-RS Pinocchio guide state 0.1 mm repeatability, while the main repository table states less than 0.2 mm. The course lists a 6.5 kg arm weight, whereas the repository and Pinocchio guide list approximately 6.7 kg.[1][3][5] Because the sources do not explain whether these differences reflect measurement methods or a hardware revision, the values should not be combined into a single definitive specification.

## Control and software stack

The reBot repositories separate low-level motor access from higher-level robotics tools. The Python control layer handles motor communication and arm configuration. Seeed supplies guides for kinematics and gravity compensation with Pinocchio, motion planning through ROS and MoveIt, camera-based grasping examples, LeRobot data collection, and USD-based simulation.[1][6]

[NVIDIA](https://aiwiki.ai/wiki/nvidia)'s Isaac Teleop documentation includes a `rebot_devarm_leader` plugin for the seven reBot joints. The plugin selects the Damiao backend when given a serial-device path and the RobStride backend when given a SocketCAN interface such as `can0`. NVIDIA documents the Damiao build as seven MIT-protocol motors behind a USB-to-CAN adapter. The RobStride path uses classic CAN at 1 Mbps and is Linux-only. Both paths can disable motor torque so an operator can back-drive the arm while joint positions are read for teleoperation.[7]

For B601-DM, Seeed also publishes an Isaac Sim bridge for synchronizing a physical or manually driven arm with a simulated model. Its documented modes include joint tests without physical hardware, inverse kinematics, minimum-jerk joint-space trajectories, gravity-compensated manual input, and passive real-to-sim joint mirroring. In the documented setup, sender and receiver processes exchange joint data over UDP.[6]

NVIDIA's official Isaac-GR00T repository contains a separate B601-DM example. It converts a LeRobot dataset, fine-tunes [NVIDIA Isaac GR00T N1.7](https://aiwiki.ai/wiki/groot_n1_7) as a new embodiment, performs open-loop evaluation, and provides a closed-loop client example for the physical arm.[8] This published example is specific to the Damiao build. The B601-RS course described below supplies its own RS-specific configuration rather than establishing that every reBot configuration is interchangeable.

## Sim-to-real VLA course

On September 1, 2026, NVIDIA Robotics announced a course developed by Seeed Studio in collaboration with NVIDIA's robotics team. The course, titled *Learning Physical AI: A Sim-to-Real VLA Pipeline with Seeed reBot Arm and NVIDIA Isaac*, uses the B601-RS as its physical follower arm. Its public interface lists 19 modules in five chapters and more than 20 hours of intermediate-level material.[3][9]

The course organizes a [sim-to-real transfer](https://aiwiki.ai/wiki/sim_to_real_transfer) workflow around a stationery pick-and-place task. It covers physical and simulated demonstration collection, optional synthetic scene augmentation, post-training of a [vision-language-action model](https://aiwiki.ai/wiki/vision_language_action_model), real-arm evaluation, and edge deployment. These are tutorial stages, not evidence that one trained policy will transfer to every task or environment.[3]

### Demonstration collection

For physical data collection, the course uses a back-drivable leader arm to command a B601-RS follower through LeRobot. Its example records two 640 by 480 camera streams at 30 frames per second, joint and gripper data, and a language task label. The sample command requests 100 episodes of a 25-second stationery-organization task. Those values define the tutorial example and are not minimum dataset requirements established by a controlled study.[3]

The simulated-data module is explicitly optional. It uses a reBot leader to control a B601 model in Isaac Sim and stores actions, simulated joint states, wrist and side-camera images, task text, randomization parameters, and task-success status in LeRobot Dataset v3 format. The module also supports replaying recorded actions in simulation. Replay checks the motion trace; it is not an evaluation of physical transfer.[3]

### Augmentation and GR00T post-training

An optional augmentation module uses NVIDIA Cosmos 3 Transfer to change scene appearance while retaining the source manipulation video. The course combines a source video with Canny edges, SAM 2 segmentation, and a text description of the target background. It tells learners to inspect generated videos for robot deformation, object inconsistency, temporal flicker, and viewpoint errors before adding them to training data.[3]

The training module converts its LeRobot v3 data to the v2 layout expected by the illustrated GR00T workflow, maps six arm joints and the gripper into a seven-dimensional state and action representation, and fine-tunes GR00T N1.7 as a new embodiment. GR00T's published research describes the model family as a dual-system VLA architecture: a vision-language component interprets images and instructions, and a diffusion-transformer component generates actions.[10] That paper evaluates GR00T N1 across other embodiments. It does not report results for reBot, so its benchmark claims cannot be transferred to the B601 series.

### Physical evaluation and edge deployment

The course's physical-evaluation module separates model inference from robot control through a policy server and client. Its demonstration reports autonomous manipulation by the trained arm, but the page does not publish episode counts, success rates, comparison baselines, failure categories, or statistical uncertainty.[3]

The deployment chapter describes exporting the trained policy to TensorRT for [NVIDIA Jetson](https://aiwiki.ai/wiki/nvidia_jetson) systems. Its example builds seven target-specific engines and warns that engine files cannot be copied between Jetson AGX Orin and [Jetson Thor](https://aiwiki.ai/wiki/jetson_thor). Engine compatibility depends on the GPU architecture, TensorRT version, checkpoint, graph shapes, precision, and builder configuration.[3]

## Limitations

Most detailed evidence about reBot comes from Seeed Studio, its repositories, and documentation produced with NVIDIA. The NVIDIA Isaac Teleop integration and Isaac-GR00T example independently confirm that the arm has concrete integration paths in NVIDIA's software, but they do not establish mechanical durability, payload performance over time, calibration stability, or general policy reliability.[7][8]

The September course is a reproducible tutorial rather than a peer-reviewed benchmark. It demonstrates one stationery task and gives no controlled comparison between real-only, simulation-assisted, and Cosmos-augmented training. It also does not quantify the sim-to-real gap or report how often the physical task succeeds. Claims that the workflow improves robustness or reduces data requirements therefore remain unverified for reBot.[3]

Users must also reconcile version-specific dependencies. The course pins particular Isaac Sim, LeRobot, GR00T, camera, and Jetson environments, while the standalone reBot documentation continues to evolve. Hardware limits, motor configuration, joint calibration, camera mapping, CAN setup, inference latency, and emergency-stop procedures remain part of deployment. A successful simulation or vendor demonstration is not a substitute for task-specific safety validation on the physical arm.[3][5][6]

## References

1. Seeed Studio. "Seeed-Projects/reBot-DevArm." GitHub repository and README. Accessed September 1, 2026. https://github.com/Seeed-Projects/reBot-DevArm
2. Open Source Hardware Association. "reBot Arm B601," certification CN000024. Certified May 14, 2026. https://certification.oshwa.org/cn000024.html
3. Seeed Studio and NVIDIA Robotics. "Learning Physical AI: A Sim-to-Real VLA Pipeline with Seeed reBot Arm and NVIDIA Isaac." Course modules. Accessed September 1, 2026. https://www.seeedstudio.com/sim-to-real-with-seeed-rebot-and-nvidia-isaac
4. Seeed Studio. "reBot DevArm Open Source Hardware Specification: reBot Arm B601 RS." GitHub. Initial hardware upload dated July 9, 2026. https://github.com/Seeed-Projects/reBot-DevArm/blob/main/hardware/reBot_B601_RS/README.md
5. Seeed Studio. "Getting Started with Pinocchio and MeshCat for reBot Arm B601-RS." Accessed September 1, 2026. https://wiki.seeedstudio.com/rebot_arm_b601_rs_pinocchio_meshcat/
6. Seeed Studio. "Simulating reBot-B601-DM with Isaac Sim." Accessed September 1, 2026. https://wiki.seeedstudio.com/rebot_arm_b601_dm_isaacsim/
7. NVIDIA. "Generic Joint-Space Device: The reBot DevArm leader plugin." Isaac Teleop 1.4 documentation. Updated August 21, 2026. https://nvidia.github.io/IsaacTeleop/release/1.4.x/device/joint_space.html
8. NVIDIA. "Finetuning GR00T N1.7 Model for Seeed reBot Arm B601 DM." Isaac-GR00T repository. Accessed September 1, 2026. https://github.com/NVIDIA/Isaac-GR00T/blob/main/examples/rebot-arm-dm/README.md
9. NVIDIA Robotics. X post announcing the Seeed reBot Arm and NVIDIA Isaac course. September 1, 2026. https://x.com/NVIDIARobotics/status/2094850247184826558
10. NVIDIA et al. "GR00T N1: An Open Foundation Model for Generalist Humanoid Robots." arXiv:2503.14734, revised March 27, 2025. https://arxiv.org/abs/2503.14734