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Benchmarks of state-of-the-art rigid-body dynamics libraries report betterperformance solving the inverse dynamics problem than the forward alternative.Those benchmarks encouraged us to question whether that computational advantagewould translate to direct transcription, where calculating rigid-body dynamicsand their derivatives accounts for a significant share of computation time. Inthis work, we implement an optimization framework where both approaches forenforcing the system dynamics are available. We evaluate the performance ofeach approach for systems of varying complexity, for domains with rigidcontacts. Our tests reveal that formulations using inverse dynamics convergefaster, require less iterations, and are more robust to coarse problemdiscretization. These results indicate that inverse dynamics should bepreferred to enforce the nonlinear system dynamics in simultaneous methods,such as direct transcription.

Inverse Dynamics vs. Forward Dynamics in Direct Transcription Formulations for Trajectory Optimization