> For AI agents: guidance on navigating Viam documentation is available at https://docs.viam.com/llms.txt. # Wrap-up and next steps Review what you built in the SO-ARM101 palletizing workshop, the parts of the Viam platform you exercised, and where to take your solution next. > Source: https://docs.viam.com/tutorials/so-arm101-palletizing/wrap-up/ Congratulations, you have finished the SO-ARM101 palletizing workshop! Starting from an empty machine, you configured a real desktop arm, taught it the cell by hand, and wrote the code that packs cubes onto a pallet. ## What you built - **Milestone one.** You drove the arm through a static bottom-layer pack from your own Python script, proving your connection, your configured resources, and the poses you taught by hand all hold up under real code. - **Milestone two.** You closed the loop with obstacle avoidance: the motion service plans a collision-free path around the cubes already on the pallet and the cube in the gripper, so the second layer stacks cleanly on top of the first. ## What you exercised on the platform This workshop was small on purpose, but it touched most of the moving parts you will use on any Viam machine: - **Configuration and runtime:** the Viam app as the single source of truth (the CONFIGURE tab and its JSON view), `viam-server` running your resources, and the module system, including a discovery service that suggested the arm and gripper configuration for you. - **Resources:** the arm and gripper components from the SO-ARM101 module, with the gripper attached to the arm through the frame system. - **Frame system and motion:** placing the arm at the world origin so hand-taught poses are measured from its base, teaching real-world gripper anchor poses by back-driving the arm with torque disabled, and letting the motion service plan for the gripper's fingertips. You also saw how a WorldState of placed cubes and the held cube keeps the planner from routing through the stack. - **Code:** the Python SDK (`RobotClient`, the typed `Gripper` and `MotionClient`, and `motion.move`), built up one method at a time into `palletizer.py`. ## Where to go next Everything above is a foundation you can build on. A few directions, each with a starting point in the docs: - **Extend the pack.** Add more layers, change the grid pattern, or force a straight-line descent into each cell with a [motion constraint](/motion-planning/move-an-arm/move-with-constraints/). - **Add perception.** Replace the fixed staging spot with a camera that finds cubes: [capture and sync images](/data/capture-sync/), [build a dataset and train a model](/train/train-a-model/), then deploy it through the [ML model vision service](/reference/services/vision/mlmodel/) so the arm picks whatever it sees. - **Build an interface.** Put a browser UI in front of the cell with a [Viam application](/build-apps/): start a pack and watch progress from a dashboard instead of a terminal. - **Operationalize it.** Reuse this configuration across machines with a [fragment](/hardware/fragments/) and [capture data](/data/capture-sync/) from every run. - **Explore the rest of the platform.** The same patterns work from other [SDKs](/reference/sdks/) (Go, TypeScript, C++, Flutter) and across the full [component and service APIs](/reference/apis/). When you are ready to build on your own hardware, the [Viam documentation](/) and the [module registry](https://app.viam.com/registry) are where to start.