Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Researchers · Robot Arm Controller

Robot Arm Controller for a robotic hand

Built for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model. Simulate a robotic hand live below — adjust the inputs and watch it respond, right in your browser.

Forward Kinematics

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Forward KinematicsLive

joint angles → end effector

Controls

Presets

Joint 1 angle (°)

Joint 2 angle (°)

Joint 3 angle (°)

Forward kinematics computes where a robot arm's tip ends up from its joint angles. Each link rotates relative to the previous one, so the transforms chain together. It is fast and unique — every set of angles gives exactly one end-effector position — which is why controllers use it constantly.

▶ Run in Python

Data Inspector

End X172.2

End Y110.4

Reach204.6

Links3

Governing equation

x = i \sum L_{i} cos (j \leq i \sum θ_{j}) = 172.2, y = i \sum L_{i} sin (j \leq i \sum θ_{j}) = 110.4

Reading this result: Reach 205 sits near the 210 maximum: the three links are nearly collinear, so the arm is almost fully extended.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Inverse Kinematics

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Inverse KinematicsLive

target → joint angles

Controls

Presets

Link 1 length

Link 2 length

Elbow-up solution

Inverse kinematics is the hard direction: given where you want the tip, find the joint angles. For a 2-link arm the law of cosines gives a clean closed form — but with two solutions (elbow-up or elbow-down) and none at all when the target is out of reach. Drag the target, or click anywhere to move it there.

▶ Run in Python

Data Inspector

Joint 1-20.8°

Joint 291.4°

Target dist126

Total reach180

Reachableyes

Governing equation

θ_{2} = arccos \frac{x ^{2} + y ^{2} - 10 0 ^{2} - 8 0 ^{2}}{2 \cdot 100 \cdot 80} = 91. 4^{\circ}, θ_{1} = atan2 (40, 120) - atan2 (80 sin θ_{2}, 100 + 80 cos θ_{2}) = - 20. 8^{\circ}

Reading this result: This 2-link arm (segments 100 and 80, total reach 180) has a closed-form inverse: the law of cosines gives the joint angles that place the tip exactly on the target, with two branches — elbow-up and elbow-down. Iterative methods like CCD or the Jacobian transpose converge to one of them.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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PID Control

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PID ControllerLive

step response tuning

Controls

Presets

Proportional Kp

Integral Ki

Derivative Kd

A PID controller drives a system to its setpoint using three terms: proportional reacts to the current error, integral eliminates steady-state offset, and derivative damps overshoot. Tuning the three gains trades off speed, overshoot, and stability — the workhorse of industrial control.

▶ Run in Python

Data Inspector

Overshoot0%

Settling time0.00 s

Steady-state error0.000

Governing equation

u (t) = 4.0 e (t) + 1.00 \int e d t + 0.5 \frac{d e}{d t}

Reading this result: Balanced gains — proportional for speed, integral to remove offset, derivative to damp overshoot: the textbook well-tuned response.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Frequently asked questions

Is this good for researchers?: Yes — this version of "Robot Arm Controller for a robotic hand" is framed for researchers prototyping or validating an idea. Prototype fast, reproduce exactly, and share a citable, interactive version of your model.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.