Robotic Arm Lab — Chapter 5 (IK V2 BEAST)

Robotic Arm Lab

Chapter 5 — IK V2 (Moving Target + Jitter + CCD Spotlight)

Main Concept must remember

IK = Continuous Error Correction

Robot tries to make the error line (hand → target) become zero.

One line to remember: IK minimizes error (like loss minimization in ML).

Target Motion fixed, auto-moving

Pattern

orbit, 8, spiral

Speed

slow → fast

Direction

-1 to +1

Radius

0.8m to 3.2m

Boss Fight Mode

speed ramps up

Goal: keep error small even when target moves fast. That’s real IK mastery.

IK Solver Controls jitter lives here

Auto Solve

controller loop

Iterations / Frame

1 to 60

Step Gain (Jitter Knob)

0.03 to 0.90

Inertia

servo memory

Damping

0 to 1

Max Joint Speed

limit safety

Hotkeys: Space auto on/off • S step cycle • R reset

Robot Setup realistic constraints

Link-1 Length

0.8m to 2.4m

Link-2 Length

0.6m to 2.2m

Link-3 Length

0.4m to 1.6m

Joint Limits

realism

Solve Plane

learning mode

First master 2D IK. Then we go full 3D + Jacobian later.

Visual Explain Mode cinematic learning

CCD Spotlight

algorithm visible

Show Error Vector

hand→target

Show Workspace

limits visible

Show Trace

path history

Ghost Pose

before/after

Want to SEE jitter? Set Gain high, Damping 0, Inertia high.

State: —

Error: —

Tip: Gain↑ + Damping↓ = jitter

IK V2: Moving Target + CCD Spotlight

Main concept: Error = distance(EndEffector, Target). IK keeps updating angles to reduce this error.

Why jitter happens: if your “step gain” is too aggressive and damping is low, the solver overshoots and keeps correcting.

Selected: None

Hover parts for easy explanation. Click to lock selection.

Live Math (always visible):

—

This lab’s “math heartbeat” is the shrinking error.

Error Graph (last ~6 seconds):

If the graph oscillates: that’s jitter/overshoot.

Live status:

—

Expert Missions:

1) Keep error tiny on Orbit (slow).
2) Switch to Figure-8 (medium).
3) Turn on Boss Fight and keep error stable by tuning Gain/Damping/Iters.
4) Turn Joint Limits ON and learn real robotics constraints.