Inversemodellen

Inversemodellen, or the inverse model, is a concept used in neuroscience, robotics, and machine learning to describe a mapping from desired outcomes to the actions required to achieve them. It is the functional inverse of a forward model, which predicts the consequences of a given action.

In motor control and human movement science, the inversemodellen converts a desired movement or endpoint into the motor commands (such as joint torques or muscle activations) needed to realize it. It is often discussed together with the forward model, forming a loop that supports action planning and control.

In robotics and control engineering, the inversemodellen computes actuator commands from a target pose or trajectory. The problem is typically nonlinear, high-dimensional, and can be ill-posed or underdetermined because multiple joint configurations can produce the same end-effector position. Techniques include analytical inverse kinematics, optimization-based control, and learning-based models such as neural networks. Regularization and the use of a forward model to validate predictions are common.

In data-driven contexts, inverse modeling aims to infer inputs from observed outputs. This raises issues of non-uniqueness, sensitivity to measurement noise, and model misspecification. Probabilistic formulations and Bayesian methods are often employed to quantify uncertainty in the estimated actions.

Historically, inversemodellen arose in control theory and cognitive science and remains central in rehabilitation robotics, autonomous systems, and AI. It is closely related to inverse kinematics and to motor learning, where practice helps calibrate the inverse mapping.