Progression Plan

• 0) ROS2 + Arm Bringup (Simulation First) — Create a ROS2 workspace, bring up the arm in RViz/Gazebo with a clean launch pipeline.

• 1) URDF/Xacro Modeling of Your Arm — Build the arm model (links/joints, limits, inertias) and validate it in RViz with TF.

• 2) ros2_control Hardware Interface (Real Arm) — Implement the driver layer (joint states + joint commands) so ROS2 can read/command the physical motors.

• 3) Basic Joint Control (Position/Velocity Control) — Move single joints and then multi-joint trajectories with safety limits and smooth interpolation.

• 4) Calibration + Zeroing + Repeatability Checks — Establish homing, joint offsets, and repeatability tests so the arm behaves predictably every run.

• 5) Forward Kinematics (FK) + TF Verification — Compute end-effector pose from joint angles and verify it matches RViz/TF numerically.

• 6) Inverse Kinematics (IK) for Target Poses — Solve “move end-effector to (x,y,z,orientation)” using a practical IK approach for your arm.

• 7) Trajectory Generation (Time-Parameterized Motion) — Generate smooth joint trajectories (limits-aware) and execute them reliably on real hardware.

• 8) Dynamics Basics (Why Motion Feels Different in Reality) — Introduce torque/load, gravity effects, and tuning considerations that impact real motion quality.

• 9) MoveIt 2 Integration (Planning Pipeline) — Use MoveIt 2 for planning + execution, including planning scene, collision objects, and constraints.

• 10) Motion Planning with Obstacles (Collision Avoidance) — Plan around simple obstacles in simulation, then mirror the same planning scene on the real arm.

• 11) Pick/Place “Fixed Targets” (First Manipulation) — Execute repeatable pick-and-place using pre-defined object poses and approach/retreat motions.

• 12) Perception Setup (RGB-D Camera in ROS2) — Integrate a depth camera, publish point clouds, and align camera-to-arm calibration (extrinsics).

• 13) Scene Mapping for Manipulation (Voxel/Octomap) — Build a 3D scene representation and feed it to planning for runtime collision awareness.

• 14) Runtime Object Picking (Pose from Depth) — Detect an object, estimate its 6D pose from depth, plan grasp, and execute pick-and-place online.

• 15) QR Code-Based Pick/Place (Structured Perception) — Use QR tags to get robust IDs + poses for teaching structured automation workflows.

• 16) Failure Handling + Recovery Behaviors — Add “retry grasp,” “re-localize,” “safe retreat,” and “home” behaviors for real-world robustness.

• 17) Data Logging + Evaluation Harness — Record ROS bags, define success metrics (time, accuracy, retries), and build a repeatable test suite.

• 18) RL for Motion Primitives (Simulation to Real Concepts) — Train a small policy for a constrained skill (e.g., insertion/approach) in sim, then discuss safe transfer.

• 19) VLA / “Language-to-Action” Task Layer — Add a high-level instruction interface that selects skills (“pick red block,” “place in bin”) while ROS2 executes safely.

• 20) Capstone: “Perception-Driven Autonomous Workcell” — End-to-end system: camera → mapping → object pose → planning → pick/place → logging + recovery.