Principle:Google deepmind Mujoco Inverse Dynamics
| Knowledge Sources | Domains | Last Updated |
|---|---|---|
| Google DeepMind MuJoCo | Physics Simulation | 2025-02-15 |
Overview
Description: Inverse dynamics computes the forces and torques required to produce a given motion trajectory. Given positions, velocities, and accelerations, MuJoCo recovers the generalized forces, actuator forces, and constraint forces that would produce the observed motion.
Context: Inverse dynamics is used for motion analysis, trajectory validation, and computing reference forces for control. MuJoCo's inverse dynamics pipeline mirrors the forward dynamics pipeline in reverse: given the kinematic state and desired accelerations, it solves for the forces.
Theoretical Basis
Inverse dynamics is based on Newton-Euler equations applied in reverse:
- Recursive Newton-Euler Algorithm (RNEA): A two-pass O(n) algorithm that computes joint torques from desired accelerations by propagating velocities outward and forces inward through the kinematic tree
- Force decomposition: The total generalized force is decomposed into inertial, Coriolis, gravitational, and external components
- Constraint force recovery: Given the total required force and the applied actuator forces, constraint forces (contacts, joint limits) can be inferred
Inverse dynamics provides a computationally efficient check on forward simulation and enables feedforward control design.
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