{"id":131440,"date":"2026-08-17T06:35:07","date_gmt":"2026-08-17T06:35:07","guid":{"rendered":"https:\/\/www.seeedstudio.com\/blog\/?p=131440"},"modified":"2026-08-17T06:47:56","modified_gmt":"2026-08-17T06:47:56","slug":"how-low-cost-teach-a-robot-arms-to-work-on-their-own","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2026\/08\/17\/how-low-cost-teach-a-robot-arms-to-work-on-their-own\/","title":{"rendered":"How Low\u2011Cost Teach a Robot Arms to Work on Their Own ?"},"content":{"rendered":"\n<p>Today, we&#8217;re diving into a topic that anyone working in embodied AI will eventually encounter: <strong>Imitation Learning<\/strong>.<\/p>\n\n\n\n<p>In this tutorial, you&#8217;ll learn the complete workflow for teaching a robotic arm to perform tasks\u2014from <strong>calibration \u2192 teleoperation \u2192 dataset collection \u2192 <\/strong><strong>ACT<\/strong><strong> training \u2192 real-world deployment<\/strong>.<\/p>\n\n\n\n<p>Even if you&#8217;re new to robotics and machine learning, you can follow this guide to build and reproduce an ALOHA-style bimanual robot learning project.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">1. Project Demo<\/h2>\n\n\n\n<p>The video demonstrates the trained imitation learning policy, <strong>ACT<\/strong>, running on the SO-ARM101 in the real world. <\/p>\n\n\n\n<figure class=\"wp-block-video\"><video controls src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/soarm-training-result.mov\"><\/video><\/figure>\n\n\n\n<p>The robot is no longer controlled by a human operator. Instead, the trained policy directly generates continuous action sequences and enables the robot to autonomously perform precise pick-and-place tasks.<\/p>\n\n\n\n<div class=\"wp-block-blockspare-blockspare-notice-bar blockspare-520f01fe-eb69-4 blockspare-notice-block aligncenter\" blockspare-animation=\"\"><style>.blockspare-520f01fe-eb69-4 .blockspare-noticebar-wrapper{background-color:#f9f9f9;border-color:#3c1a5b !important;padding-top:10px;padding-right:20px;padding-bottom:10px;padding-left:20px;margin-top:30px;margin-right:0;margin-bottom:30px;margin-left:0;border-radius:0}.blockspare-520f01fe-eb69-4 .blockspare-notice-content{color:#4c4c4c;font-size:18px!important}@media screen and (max-width:1025px){.blockspare-520f01fe-eb69-4 .blockspare-notice-content{font-size:16px!important}}@media screen and (max-width:768px){.blockspare-520f01fe-eb69-4 .blockspare-notice-content{font-size:14px!important}}<\/style><div class=\"blockspare-noticebar-wrapper blockspare-blocks blockspare-hover-item\"><div class=\"blockspare-section-notice-wrap\"><div class=\"blockspare-title-wrap\"><div><div class=\"blockspare-notice-content\"><i class=\"fas fa-star\"><\/i><strong>Want to start training right away? Check this Wiki tutorial: <\/strong> <a href=\"https:\/\/wiki.seeedstudio.com\/cn\/lerobot_double_arm_so_arm_training\/\">https:\/\/wiki.seeedstudio.com\/cn\/lerobot_double_arm_so_arm_training\/<\/a><\/div><\/div><\/div><\/div><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">2. What Will You Learn?<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Understand the core concepts behind <strong>leader-follower teleoperation<\/strong> and how demonstration data is collected for imitation learning.<\/li>\n\n\n\n<li>Follow the complete workflow from <strong>servo setup and calibration to teleoperation, dataset creation, model training, and deployment<\/strong>.<\/li>\n\n\n\n<li>Use the open-source <strong>LeRobot framework<\/strong> to train ACT (Action Chunking with Transformers) policies.<\/li>\n\n\n\n<li>Build your own <strong>low-cost bimanual embodied AI platform<\/strong> and extend it to a wide range of manipulation tasks.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">3. Things Used in This Project<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><img decoding=\"async\" src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/SO-ARM101-Assemble-Kit-1030x773.png\" alt=\"SO-ARM101 Assemble Kit\" style=\"\">SO-ARM101 Assemble Kit<\/td><td><a href=\"https:\/\/www.seeedstudio.com\/SO-ARM-101-Assembled-Kit-Pro-p-6691.html\">https:\/\/www.seeedstudio.com\/SO-ARM-101-Assembled-Kit-Pro-p-6691.html(Each kit includes one Leader arm and one Follower arm)<\/a><\/td><td>\u00d72<\/td><\/tr><tr><td><img decoding=\"async\" src=\"https:\/\/seeedstudio.feishu.cn\/space\/api\/box\/stream\/download\/asynccode\/?code=N2JmNGQwMzgwZGY1YWEzODEyNjIwYzZlOWE0MzcxM2Jfck9pemRqUU1rZjhVd2doQ1c3SmFuem9SdXVvRXZobFNfVG9rZW46QXFXaGJpMUs1bzUzdm94NGc0T2NwakdEbmhiXzE3ODY5NDU1MTU6MTc4Njk0OTExNV9WNA&amp;add_watermark=true&amp;scene_type=CCM\" style=\"\" alt=\"USB Camera\">USB Camera<\/td><td><a href=\"https:\/\/www.seeedstudio.com\/ET-S231-90-USB-Camera-p-6684.html\">https:\/\/www.seeedstudio.com\/ET-S231-90-USB-Camera-p-6684.html<\/a><\/td><td>\u00d7 1~2\uff08The exact number depends on the training task\uff09<\/td><\/tr><tr><td><img decoding=\"async\" src=\"https:\/\/seeedstudio.feishu.cn\/space\/api\/box\/stream\/download\/asynccode\/?code=ZTZiMWZjOWIwYTI3MDRhOGRhYWJlZmY2ZmFhZWZlYWRfMXlDS3lDZ0tPRDUwQUJGbFZGVkFlb3NiV2lGREZ0VTFfVG9rZW46RVg0M2JzaDRzbzcxMnh4dVQ4c2NRQ2RQbnRjXzE3ODY5NDU1Mjg6MTc4Njk0OTEyOF9WNA&amp;add_watermark=true&amp;scene_type=CCM\" style=\"\" alt=\"PC \/ NVIDIA Edge Computing Platform\">PC \/ NVIDIA Edge Computing Platform<\/td><td><a href=\"https:\/\/www.seeedstudio.com\/reComputer-Robotics-J4012-with-GMSL-extension-board-p-6537.html\">https:\/\/www.seeedstudio.com\/reComputer-Robotics-J4012-with-GMSL-extension-board-p-6537.html<\/a><\/td><td>\u00d71<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>The SO-ARM101 is officially supported by the LeRobot ecosystem, which means you can <strong>avoid a lot of low-level hardware and driver integration work<\/strong> and <strong>get started much faster<\/strong>.<\/p>\n\n\n\n<p>Here are some of its key advantages:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Ready-to-use leader-follower architecture:<\/strong> Designed for teleoperation out of the box, with no need to develop your own low-level drivers.<\/li>\n\n\n\n<li><strong>A strong community and ecosystem:<\/strong> Standardized imitation learning dataset formats, extensive community resources, and detailed tutorials make it accessible to beginners.<\/li>\n\n\n\n<li><strong>CAN FD<\/strong><strong> servo motors:<\/strong> Stable, low-latency communication makes the platform suitable for synchronized, high-quality data collection.<\/li>\n\n\n\n<li><strong>Fully <\/strong><strong>open source<\/strong><strong>:<\/strong> Includes mechanical STP files, hardware BOMs, and software SDKs.<\/li>\n\n\n\n<li><strong>Affordable:<\/strong> Significantly more accessible than traditional research-grade bimanual robot platforms.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">4. Key Concepts<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">4.1 Software Framework \u2014 LeRobot<\/h3>\n\n\n\n<p>LeRobot brings together what would otherwise be a collection of separate components\u2014<strong>data collection, model training, policy evaluation, and real-robot deployment<\/strong>\u2014under a unified interface.<\/p>\n\n\n\n<p>At its core, LeRobot is a Python library. Once installed with <code>pip<\/code>, most operations can be performed through command-line tools or a few lines of Python, making it particularly convenient for developers who don&#8217;t want to spend too much time dealing with low-level robotics infrastructure.<\/p>\n\n\n\n<p><strong>The main components of LeRobot include:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Datasets:<\/strong> Provides standardized dataset formats containing multi-camera images, joint states, action commands, and other information. It also handles time synchronization and data processing.<\/li>\n\n\n\n<li><strong>Policies:<\/strong> Includes popular robot learning algorithms such as ACT, Diffusion Policy, and TDMPC. You can select the desired policy through configuration rather than implementing the entire model architecture from scratch.<\/li>\n\n\n\n<li><strong>Trainer:<\/strong> Handles the training loop, learning-rate scheduling, checkpointing, resuming training, TensorBoard logging, and other common training tasks.<\/li>\n\n\n\n<li><strong>Envs:<\/strong> Provides unified interfaces for real robots and simulation environments, making it easier to move trained policies between simulation and physical robots.<\/li>\n\n\n\n<li><strong>Scripts:<\/strong> Provides ready-to-use command-line tools for teleoperation, dataset recording, training, model export, and real-robot rollouts.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">4.2 Imitation Learning Policy \u2014 ACT<\/h3>\n\n\n\n<p>The basic imitation learning pipeline is straightforward:<\/p>\n\n\n\n<p><strong>Human demonstration \u2192 Data collection \u2192 Model training \u2192 Autonomous execution<\/strong><\/p>\n\n\n\n<p>For tabletop manipulation, <strong>ACT<\/strong><strong> (Action Chunking with Transformers)<\/strong> has become one of the most widely used policy architectures.<\/p>\n\n\n\n<p>ACT was introduced by researchers at Stanford alongside the ALOHA system. Its key idea is <strong>action chunking<\/strong>.<\/p>\n\n\n\n<p>Instead of predicting only one action at a time, which can cause small errors to accumulate over a long sequence, ACT predicts a sequence of future actions\u2014a so-called <strong>action chunk<\/strong>\u2014and executes these actions as a coherent sequence.<\/p>\n\n\n\n<p>This helps reduce the error accumulation associated with purely single-step action prediction.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1030\" height=\"658\" src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Action-Chunking-with-Transformers-architecture-1030x658.png\" alt=\"\" class=\"wp-image-131443\" srcset=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Action-Chunking-with-Transformers-architecture-1030x658.png 1030w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Action-Chunking-with-Transformers-architecture-300x192.png 300w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Action-Chunking-with-Transformers-architecture-768x491.png 768w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Action-Chunking-with-Transformers-architecture-1536x982.png 1536w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Action-Chunking-with-Transformers-architecture-32x20.png 32w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Action-Chunking-with-Transformers-architecture-1024x655.png 1024w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Action-Chunking-with-Transformers-architecture.png 1633w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/figure>\n\n\n\n<p>ACT has several important advantages:<\/p>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Smoother motion:<\/strong> Predicting a sequence of actions helps reduce the jitter that can occur when making independent frame-by-frame decisions.<\/li>\n\n\n\n<li><strong>Improved robustness:<\/strong> Action chunking can reduce error accumulation and make long-horizon manipulation tasks more stable.<\/li>\n\n\n\n<li><strong>Multimodal behavior modeling:<\/strong> ACT uses a CVAE-based architecture to model multiple valid ways of performing the same task.<\/li>\n<\/ol>\n\n\n\n<p>As a commonly supported policy in robot learning frameworks such as LeRobot, ACT has been demonstrated on a variety of fine-grained manipulation tasks, including object grasping, component assembly, and cloth manipulation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">5. Complete Hands-On Workflow: Six Steps to Bimanual Imitation Learning<\/h2>\n\n\n\n<p>Follow the Wiki tutorial to reproduce the teleoperation and training workflow.<\/p>\n\n\n\n<div class=\"wp-block-blockspare-blockspare-container alignfull blockspare-40e1d139-02de-4\" blockspare-animation=\"\"><style>.blockspare-40e1d139-02de-4 > .blockspare-block-container-wrapper{background-color:#f9f9f9;padding-top:20px;padding-right:20px;padding-bottom:20px;padding-left:20px;margin-top:30px;margin-right:0px;margin-bottom:30px;margin-left:0px;border-radius:0}.blockspare-40e1d139-02de-4 .blockspare-image-wrap{background-image:none}<\/style><div class=\"blockspare-block-container-wrapper blockspare-hover-item\"><div class=\"blockspare-container-background blockspare-image-wrap has-background-opacity-100 has-background-opacity\"><\/div><div class=\"blockspare-container\"><div class=\"blockspare-inner-blocks blockspare-inner-wrapper-blocks\">\n<p><i class=\"fas fa-book\"><\/i> Follow the Wiki tutorial to reproduce the teleoperation and training workflow\uff1a<a href=\"https:\/\/wiki.seeedstudio.com\/lerobot_so100m_new\/\">https:\/\/wiki.seeedstudio.com\/lerobot_so100m_new\/<\/a><\/p>\n<\/div><\/div><\/div><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Step 0 \u2014 Set Up the LeRobot Environment<\/h3>\n\n\n\n<p>First, install the required software dependencies.<\/p>\n\n\n\n<p>The tutorial provides installation instructions for both <strong>Jetson<\/strong> and <strong>Ubuntu<\/strong>, including the required packages and commands.<\/p>\n\n\n\n<p>The goal of this step is to prepare the complete software environment for robot control, data collection, and model training.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1030\" height=\"684\" src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-0-\u2014-Set-Up-the-LeRobot-Environment-1030x684.png\" alt=\"\" class=\"wp-image-131444\" srcset=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-0-\u2014-Set-Up-the-LeRobot-Environment-1030x684.png 1030w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-0-\u2014-Set-Up-the-LeRobot-Environment-300x199.png 300w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-0-\u2014-Set-Up-the-LeRobot-Environment-768x510.png 768w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-0-\u2014-Set-Up-the-LeRobot-Environment-32x21.png 32w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-0-\u2014-Set-Up-the-LeRobot-Environment-1024x680.png 1024w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-0-\u2014-Set-Up-the-LeRobot-Environment.png 1383w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Step 1 \u2014 Calibrate the Robot Arms<\/h3>\n\n\n\n<p>Calibrate the joints on all four arms\u2014the two Leader arms and two Follower arms.<\/p>\n\n\n\n<p>The purpose of calibration is to establish the correct <strong>zero positions, movement directions, and operating ranges<\/strong> for each joint, allowing LeRobot to correctly interpret and control the robot.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large is-resized\"><img decoding=\"async\" width=\"1030\" height=\"816\" src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-1-\u2014-Calibrate-the-Robot-Arms-1030x816.png\" alt=\"\" class=\"wp-image-131446\" style=\"aspect-ratio:1;width:840px;height:auto\" srcset=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-1-\u2014-Calibrate-the-Robot-Arms-1030x816.png 1030w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-1-\u2014-Calibrate-the-Robot-Arms-300x238.png 300w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-1-\u2014-Calibrate-the-Robot-Arms-768x608.png 768w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-1-\u2014-Calibrate-the-Robot-Arms-32x25.png 32w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-1-\u2014-Calibrate-the-Robot-Arms-1024x811.png 1024w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-1-\u2014-Calibrate-the-Robot-Arms.png 1486w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Step 2 \u2014 Leader-Follower Teleoperation<\/h3>\n\n\n\n<p>Once calibration is complete, you can start teleoperation: move the Leader arm, and the Follower arm mirrors its movements.<\/p>\n\n\n\n<p>The <strong>teleoperation stage without cameras<\/strong> is mainly used to verify that the robot control pipeline works correctly.<\/p>\n\n\n\n<p>Once cameras are added, the system can simultaneously capture <strong>visual observations and robot actions<\/strong>, providing the data needed for imitation learning.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1030\" height=\"875\" src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-2-\u2014-Leader-Follower-Teleoperation-1030x875.png\" alt=\"\" class=\"wp-image-131445\" srcset=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-2-\u2014-Leader-Follower-Teleoperation-1030x875.png 1030w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-2-\u2014-Leader-Follower-Teleoperation-300x255.png 300w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-2-\u2014-Leader-Follower-Teleoperation-768x653.png 768w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-2-\u2014-Leader-Follower-Teleoperation-32x27.png 32w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-2-\u2014-Leader-Follower-Teleoperation-1024x870.png 1024w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-2-\u2014-Leader-Follower-Teleoperation.png 1470w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Step 3 \u2014 Dataset Recording and Management<\/h3>\n\n\n\n<p>The operator performs the task through the Leader arms while the system synchronously records what the robot <strong>sees<\/strong> and what the robot <strong>does<\/strong>.<\/p>\n\n\n\n<p>This creates the demonstration data that ACT will later learn from.<\/p>\n\n\n\n<p>In the tutorial, 50 episodes are recorded using <code>num_episodes=50<\/code>.<\/p>\n\n\n\n<p>The tutorial also covers how to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Upload datasets to the Hugging Face Hub<\/li>\n\n\n\n<li>Resume an interrupted recording session<\/li>\n\n\n\n<li>Replay recorded episodes<\/li>\n\n\n\n<li>Delete unwanted episodes<\/li>\n\n\n\n<li>Manage and inspect the collected dataset<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1030\" height=\"775\" src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-3-\u2014-Dataset-Recording-and-Management-1030x775.png\" alt=\"\" class=\"wp-image-131447\" srcset=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-3-\u2014-Dataset-Recording-and-Management-1030x775.png 1030w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-3-\u2014-Dataset-Recording-and-Management-300x226.png 300w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-3-\u2014-Dataset-Recording-and-Management-768x578.png 768w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-3-\u2014-Dataset-Recording-and-Management-32x24.png 32w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-3-\u2014-Dataset-Recording-and-Management-1024x771.png 1024w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-3-\u2014-Dataset-Recording-and-Management.png 1480w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Step 4 \u2014 Train the ACT Policy<\/h3>\n\n\n\n<p>Now comes the exciting part: training!<\/p>\n\n\n\n<p>LeRobot provides a simple training command that handles the training pipeline for you. The tutorial also introduces basic hyperparameter tuning and ways to visualize the training process.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1030\" height=\"677\" src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-4-\u2014-Train-the-ACT-Policy-1030x677.png\" alt=\"\" class=\"wp-image-131448\" srcset=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-4-\u2014-Train-the-ACT-Policy-1030x677.png 1030w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-4-\u2014-Train-the-ACT-Policy-300x197.png 300w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-4-\u2014-Train-the-ACT-Policy-768x505.png 768w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-4-\u2014-Train-the-ACT-Policy-32x21.png 32w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-4-\u2014-Train-the-ACT-Policy-1024x673.png 1024w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-4-\u2014-Train-the-ACT-Policy.png 1496w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Step 5 \u2014 Deploy the Policy on the Real Robot<\/h3>\n\n\n\n<p>Once training is complete, you can save the trained model and evaluate it on the real robot. The tutorial also covers both local model storage and Hugging Face-based workflows.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1030\" height=\"538\" src=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-5-\u2014-Deploy-the-Policy-on-the-Real-Robot-1030x538.png\" alt=\"\" class=\"wp-image-131449\" srcset=\"https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-5-\u2014-Deploy-the-Policy-on-the-Real-Robot-1030x538.png 1030w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-5-\u2014-Deploy-the-Policy-on-the-Real-Robot-300x157.png 300w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-5-\u2014-Deploy-the-Policy-on-the-Real-Robot-768x401.png 768w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-5-\u2014-Deploy-the-Policy-on-the-Real-Robot-32x17.png 32w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-5-\u2014-Deploy-the-Policy-on-the-Real-Robot-1024x535.png 1024w, https:\/\/www.seeedstudio.com\/blog\/wp-content\/uploads\/2026\/08\/Step-5-\u2014-Deploy-the-Policy-on-the-Real-Robot.png 1496w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">6. FAQ<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Q1: Do I need a GPU to train an ACT model with LeRobot?<\/h3>\n\n\n\n<p>An NVIDIA GPU with at least <strong>8 <\/strong><strong>GB<\/strong><strong> of <\/strong><strong>VRAM<\/strong> is recommended for a reasonable training speed. CPU training is possible, but it can be significantly slower.<\/p>\n\n\n\n<p>The Wiki provides more detailed hardware recommendations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q2: Is SO-ARM101 fully compatible with ALOHA-style algorithms?<\/h3>\n\n\n\n<p>Yes. The SO-ARM101 bimanual kit is designed around the ALOHA-style leader-follower teleoperation workflow and supports ACT and related robot learning workflows within the LeRobot ecosystem without requiring additional low-level integration.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q3: Can beginners learn imitation learning with this project?<\/h3>\n\n\n\n<p>Absolutely.<\/p>\n\n\n\n<p>The tutorial starts with hardware connections and walks through the workflow step by step. If you have basic Linux command-line skills, you should be able to reproduce the complete pipeline.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q4: How much does it cost to build a bimanual imitation learning platform?<\/h3>\n\n\n\n<p>The SO-ARM101 uses 3D-printed components and is designed as a low-cost robotics platform, making it an attractive option for students, researchers, and makers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q5: How many demonstrations do I need to train an ACT model?<\/h3>\n\n\n\n<p>For simple manipulation tasks, <strong>25\u201350 demonstrations<\/strong> can often provide a useful starting point, although the ideal dataset size depends heavily on task complexity, scene variation, and demonstration quality.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q6: What cameras are supported?<\/h3>\n\n\n\n<p>The system supports common USB cameras. This tutorial includes links to cameras that have been tested with the workflow.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q7: Does SO-ARM101 support robot learning frameworks other than LeRobot?<\/h3>\n\n\n\n<p>Yes. SO-ARM101 provides standard ROS\/ROS2 interfaces and a Python SDK, making it possible to integrate it with other robot learning approaches, including Diffusion Policy and VLA-based systems.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q8: How long does ACT training take?<\/h3>\n\n\n\n<p>On a mid-range GPU, a simple task with around 50 demonstrations may take roughly <strong>1\u20132 hours<\/strong> to train. Actual training time depends on the GPU, dataset size, task complexity, image resolution, and training configuration.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q9: What kinds of tasks can a bimanual robot learn?<\/h3>\n\n\n\n<p>A wide range of tabletop manipulation tasks can be explored, including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Pick-and-place<\/li>\n\n\n\n<li>Object handover<\/li>\n\n\n\n<li>Assembly<\/li>\n\n\n\n<li>Sorting<\/li>\n\n\n\n<li>Pouring<\/li>\n\n\n\n<li>Two-handed manipulation<\/li>\n<\/ul>\n\n\n\n<p>The same framework can be extended to many other tasks where demonstrations can be collected.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Q10: Do I need programming experience?<\/h3>\n\n\n\n<p>Basic Python and Linux command-line skills are helpful, but you don&#8217;t need to be an expert programmer.<\/p>\n\n\n\n<p>Seeed Studio also provides <strong>SenseCraft<\/strong>, a no-code training platform with a graphical interface designed to help beginners train robot learning models without writing code.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-4-3 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe title=\"SenseCraft One-Click Deployment SO-ARM101 Tutorial\" width=\"640\" height=\"480\" src=\"https:\/\/www.youtube.com\/embed\/jGqlyJUOEO8?start=114&#038;feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">7. Resources and Further Reading<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Getting Started with LeRobot and SO-ARM100\/SO-ARM101<strong>:<\/strong><a href=\"https:\/\/wiki.seeedstudio.com\/cn\/lerobot_so100m_new\/?utm_source=chatgpt.com\">SO-ARM100 \/ SO-ARM101 LeRobot Getting Started Guide<\/a><\/li>\n\n\n\n<li>Seeed Projects LeRobot GitHubRepository:<a href=\"https:\/\/github.com\/Seeed-Projects\/lerobot?utm_source=chatgpt.com\">Seeed Projects LeRobot<\/a><\/li>\n\n\n\n<li>Understanding ACT:<a href=\"https:\/\/huggingface.tw\/docs\/lerobot\/act?utm_source=chatgpt.com#act-action-chunking-with-transformers\">LeRobot ACT Documentation<\/a><\/li>\n\n\n\n<li>SO-ARM101 + AmazingHand Teleoperation Tutorial:<a href=\"https:\/\/wiki.seeedstudio.com\/cn\/soarm_amazinghand_teleop\/?utm_source=chatgpt.com\">SO-ARM101 AmazingHand Teleoperation Tutorial<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Today, we&#8217;re diving into a topic that anyone working in 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