Inverse Dynamics Learning

• Inverse Dynamics Learning is a framework that maps robot configurations, velocities, and accelerations to actuator torques, crucial for precise control.
• It integrates techniques from rigid-body dynamics, regression, nonparametric statistics, kernel methods, meta-learning, and deep learning to enhance model accuracy and data efficiency.
• The approach underpins advanced applications such as impedance control, model-based planning, skill transfer, and human motion analysis in robotics and biomechanics.

Inverse dynamics learning addresses the modeling of the mapping from robot states and accelerations to actuator torques or wrenches, enabling advanced control, adaptation, and model-based planning in robotics and biomechanics. Accurate inverse dynamics models enable precise tracking, impedance or computed-torque control, and facilitate both skill transfer as well as human motion analysis. The field combines principles from rigid-body dynamics, regression, nonparametric statistics, kernel methods, meta-learning, and deep learning, while aspiring to meet challenges in real-time adaptation, uncertainty quantification, compliance, and data efficiency. This article provides a rigorous exposition of key methodologies, hybrid modeling frameworks, probabilistic approaches, adaptive control integration, and critical experimental results, with all claims and algorithms substantiated in the provided primary literature.

1. Formal Statement of the Inverse Dynamics Problem

The inverse dynamics problem seeks a function $f: (q, \dot{q}, \ddot{q}) \mapsto \tau$ that maps the configuration $q \in \mathbb{R}^n$, velocity $\dot{q}$, and acceleration $\ddot{q}$ of an $n$-DOF manipulator to a vector of joint torques $\