{"id":135442,"date":"2026-11-05T13:03:38","date_gmt":"2026-11-05T13:03:38","guid":{"rendered":"https:\/\/www.seeedstudio.com\/blog\/?p=135442"},"modified":"2026-11-05T13:07:24","modified_gmt":"2026-11-05T13:07:24","slug":"act-policy-rebot-arm-imitation-learning","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2026\/11\/05\/act-policy-rebot-arm-imitation-learning\/","title":{"rendered":"Which Robotic Arms Support ACT Imitation Learning with LeRobot?"},"content":{"rendered":"\n

Seeed reBot<\/strong> Arm<\/strong> B601 supports<\/strong> ACT<\/strong> imitation learning with LeRobot through Seeed\u2019s documented integration; this guide follows the B601-RS workflow.<\/strong> Its robot adapter connects state feedback and action commands to LeRobot, while cameras provide visual observations for learning. That makes reBot a practical choice for developers who want a documented path from teaching a task to running a trained policy. SO-101<\/a> is another documented LeRobot arm option. When choosing an arm, check its adapter, observation and action interfaces, calibration, and demonstration workflow. For reBot, Seeed provides a B601-RS integration guide<\/a> and course chapters covering data collection, ACT training<\/a> and real-robot evaluation. Match the exact arm variant and software environment to those guides before starting.<\/p>\n\n\n\n

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\"Seeed<\/a><\/figure>\n\n\n\n

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Which Arms Have a Documented LeRobot ACT Workflow?<\/h2>\n\n\n\n

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Two documented options to consider are reBot Arm B601-RS and SO-101. Their integration paths differ, so compare the complete workflow rather than treating \u201cLeRobot compatible\u201d as a guarantee that any software installation will work.<\/p>\n\n\n\n

Robotic arm<\/td>Documented path<\/td>What to check<\/td><\/tr>
Seeed reBot<\/strong> Arm<\/strong> B601-RS<\/strong><\/td>Seeed\u2019s robot integration and Physical AI course connect data collection, ACT training and real-robot execution.<\/td>Follow the RS configuration, matching CAN interface, calibration and Seeed-documented environment.<\/td><\/tr>
SO-101<\/strong><\/td>Hugging Face documents the arm setup and uses SO-101 in its real-robot imitation-learning tutorial, including ACT training.<\/td>Follow the SO-101 hardware setup and the matching upstream tutorial.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

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What Is ACT?<\/h2>\n\n\n\n

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ACT<\/strong> stands for Action Chunking with Transformers.<\/strong> It learns from demonstrations that pair observations with actions. Given camera images and robot joint state, the policy predicts a sequence of future actions\u2014a chunk\u2014rather than only the next action. The controller executes actions according to the deployment configuration, then uses new observations for subsequent predictions. See the Hugging Face ACT documentation<\/a>.<\/p>\n\n\n\n

\"ACT<\/figure>\n\n\n\n

ACT<\/em> maps visual observations and joint state to an action chunk. Source:<\/em> Seeed Studio Wiki<\/a><\/em><\/p>\n\n\n\n

For example, a demonstration might show an arm approaching a block, closing its gripper, moving to a tray and releasing. The training data must represent those stages consistently. A task label describes the recording; it does not by itself turn ACT into a language-conditioned vision-language-action model.<\/p>\n\n\n\n

The original ACT project<\/a> is useful for understanding the method\u2019s research background. For a reBot implementation, follow the hardware-specific course and integration guide.<\/p>\n\n\n\n

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How to Choose an ACT-Compatible Robotic Arm<\/h2>\n\n\n\n

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Treat compatibility as a complete data-and-control path. Ask for evidence at each point:<\/p>\n\n\n\n

Selection criterion<\/td>Evidence to look for<\/td>Why it matters<\/td><\/tr>
Robot adapter<\/td>A maintained integration that reads robot state and sends actions<\/td>A policy must be able to observe and control the arm.<\/td><\/tr>
Demonstration method<\/td>A documented leader\/follower or other supported teleoperation workflow<\/td>You need a repeatable way to record the task.<\/td><\/tr>
Camera and dataset support<\/td>Defined image keys, robot-state features, actions and timing<\/td>Training and execution must agree on what each input and output means.<\/td><\/tr>
Calibration and control<\/td>Joint mapping, gripper behavior, limits and a controlled motion check<\/td>Incorrect hardware mapping cannot be fixed by a longer training run.<\/td><\/tr>
Software identity<\/td>Repository revision, adapter packages and compatible dependencies<\/td>Commands from different versions may use different APIs.<\/td><\/tr>
Task fit<\/td>Reach, gripper, workspace and other requirements checked against the exact hardware<\/td>Software compatibility alone does not establish suitability for a particular task.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

This checklist is a selection aid based on the interfaces described in Seeed\u2019s LeRobot architecture chapter<\/a>. Use the selected arm\u2019s hardware documentation to verify physical requirements.<\/p>\n\n\n\n

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Why Use Seeed reBot for ACT Imitation Learning?<\/h2>\n\n\n\n

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The strongest reason to consider reBot is its documented implementation path. You can inspect the B601 follower integration<\/a>, prepare the hardware, record demonstrations, train ACT and evaluate a saved policy within one course sequence.<\/p>\n\n\n\n

That is useful when your goal is to learn where a robot-learning experiment succeeds or fails. The B601-RS Quick Start<\/a> covers hardware preparation; the course then connects calibration, cameras, data and policy execution. You can trace a problem back to a concrete stage instead of treating every failure as a model problem.<\/p>\n\n\n\n

For the RS path, the integration guide<\/a> specifies Seeed\u2019s LeRobot repository and the robot, teleoperator and MotorBridge packages. Use that environment as a unit. The recommendation here is based on this documented learning and development workflow, not a comparative hardware benchmark.<\/p>\n\n\n\n

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