# Wrap-up and next steps

Congrats, you have finished the pick-and-place workshop! Starting from an empty machine, you configured every resource by hand and wrote the code that drives a vision-guided robot to detect a block, plan around obstacles, and place it in a bin.

## What you built

- **Milestone one.** You drove the arm through a fixed pick-and-place sequence from your own Python script, proving your connection, resources, and saved poses all hold up under real code.
- **Milestone two.** You closed the loop with live perception: the vision service detects a block, and the motion service plans a collision-free pick from the block’s camera-frame position, all from a pose your code computes each cycle.

If you completed the optional Phase 6, you also packaged that same loop as a module that runs on the robot itself.

## 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 while `viam-agent` keeps it alive, and the module system, including a discovery service that configured the camera for you.
- **Resources:** components for the arm, gripper, depth camera, and pose-saving switches; obstacles configured as components so the planner sees the table and safety walls; and services for capability, a two-stage vision pipeline (a shape detector feeding a 3D segmenter) and the builtin motion service.
- **Motion and perception:** the frame system letting the motion service plan a collision-free pick straight from a wrist-camera detection, no manual transform required; planning against your configured obstacles; and the wrist-camera rule of detecting from a known pose.
- **Code:** the Python SDK (`RobotClient`, typed component and service clients, and `motion.move`), plus packaging that same script as an inline module.

## 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 pick logic.** Sort blocks by color or shape into separate bins, add more saved poses, or force a straight-line descent with a [motion constraint](https://docs.viam.com/motion-planning/move-an-arm/move-with-constraints/).
- **Train your own detector.** Replace the shape-finder with a custom ML model: [capture and sync images](https://docs.viam.com/data/capture-sync/), [build a dataset and train a model](https://docs.viam.com/train/train-a-model/), then deploy it through the [ML model vision service](https://docs.viam.com/reference/services/vision/mlmodel/).
- **Build an interface.** Put a browser UI in front of the robot with a [Viam application](https://docs.viam.com/build-apps/): stream the camera and trigger a pick from a dashboard instead of a script.
- **Operationalize it.** Reuse this configuration across machines with a [fragment](https://docs.viam.com/hardware/fragments/) and [capture data](https://docs.viam.com/data/capture-sync/) from every run.
- **Run the module unattended.** The optional Phase 6 module runs one cycle per `do_command`. Drive that on a cadence so the robot picks on its own: an internal loop that calls `run_pick_cycle` between sleeps, or a [trigger](https://docs.viam.com/reference/triggers/) that sends `do_command` on a schedule.
- **Explore the rest of the platform.** The same patterns work from other [SDKs](https://docs.viam.com/reference/sdks/) (Go, TypeScript, C++, Flutter) and across the full [component and service APIs](https://docs.viam.com/reference/apis/).

When you are ready to build on your own hardware, the [Viam documentation](https://docs.viam.com/) and the [module registry](https://app.viam.com/registry) are where to start.