AI Robotic Arm (Vision-Guided Inverse Kinematics)
Teach a 5-DOF robotic arm to identify physical objects by color, shape, or label using computer vision and calculate smooth inverse kinematic trajectories to sort them.
The Problem
What difficult challenge is this experiment tackling?Traditional industrial robot arms require rigid, expensive pre-programmed coordinate paths that fail whenever an object is slightly misaligned on a table.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?Overhead computer vision combined with real-time inverse kinematics (IK) allows an affordable robotic arm to pick and sort randomly placed objects with 95%+ success.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human InterventionPick-and-place cycle completed in under 4.2 seconds; 97% successful grasp rate across randomly oriented parts.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsMotor torque and joint angle limiters enforced in firmware to prevent mechanical over-extension or physical impact.
Lessons Learned & Observations
Empirical findings from prototyping and testingCalibrating camera pixel space to real-world millimeter coordinates using an ArUco marker grid eliminated visual distortion.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesTrain a reinforcement learning model in simulation to handle soft or deformable objects.