Adaptive inverse kinematics that learns through hardware deformities
I want an inverse kinematics stack that stays accurate even when the arm deforms over time or during deployment: backlash, wear, manufacturing tolerances, soft mounts. If the controller can absorb those imperfections online, everyday IK applications can ship with much looser (cheaper) mechanical tolerances.
The plan centers on a partial Hebbian network for the control policy. Unlike most reinforcement learning setups that freeze after training, Hebbian plasticity keeps learning from live hardware. That means the policy can train on deformed joint data and make decisions that already account for those deformities.
In practice this sits on top of a model-based operational-space tracker, with the adaptive layer correcting for unmodeled error so the arm keeps tracking paths as the plant drifts.
Classical IK assumes the kinematic model matches the robot. Real arms diverge. An always-learning adapter turns that mismatch into something the controller can keep eating, instead of something that forces a recalibration ritual or tighter (and more expensive) machining.