Seeed Wiki Introduces Training and Real-World Validation Procedures for SO-ARM and π0.5

Seeed Wiki published a tutorial on Nov. 10, 2026, covering training and real-world validation with SO-ARM and π0.5. Using three desktop-organization tasks as examples, it addresses data collection, cloud training and validation on the physical robot. One example is placing a square box in a storage bin.

The tutorial presents a workflow for connecting tasks performed with SO-ARM to the collection and integration of instruction-labeled data, cloud training and task-by-task checks on the physical robot. For users working with multiple tasks, a practical consideration is how to combine demonstration data for each task with real-world evaluation, rather than focusing solely on model training.

The focus of the procedure is not so much explaining π0.5’s model specifications as showing the end-to-end process of collecting instruction-labeled demonstration data, combining multiple tasks into a single training dataset and checking the robot’s behavior in the real world. Of the three example tasks, placing a square box in a storage bin is identified as a specific validation target. Thus, the article describes a workflow that connects data preparation to real-world validation, not the performance of the system on a particular task. The procedure also explains VLA models, which use camera footage, task instructions and the robot’s state as inputs to predict the next action. This means that, in addition to instructions, visual information about the target object and its destination, as well as state information such as joint positions, are components of training and execution. The sequence of integrating data for multiple tasks before training, then testing the robot with different instructions, can be read as a design for distinguishing evaluation of a single-task demonstration from evaluation of switching tasks in response to instructions. However, the material presented does not specify the number of demonstrations or the amount of data for each task, success rates, or criteria for evaluating task switching. The workflow shows data collection, review and integration shaping the input for cloud training, followed by validation of the training results on the physical robot and, if needed, collection of additional data. This indicates that quality control of demonstration data and evaluation on the physical robot are part of the development process, alongside model training. The description provided does not specify the computing resources used for cloud training, criteria for deciding when to collect more data or the scope of applicability to different environments. When assessing deployment, it is important to check the amount of data and success rate for each task, as well as which stage to revisit if the robot fails in the real world.