Demonstration-Calibrated Port-Hamiltonian Retuning for Manipulation Policies
Overview
PHRetune, an offline method, improves Diffusion Policy success by 9.4 percentage points across LIBERO tasks by learning port-Hamiltonian models from demonstrations to adjust controller gains without evaluation rollouts or gain search. It outperforms alternative methods and policy-retraining baselines on real-world tasks, addressing the issue of stiffness and damping gains being inherited from data collection rather than optimized for deployment.
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- arXiv Robotics — research abstractsDemonstration-Calibrated Port-Hamiltonian Retuning for Manipulation Policies
PHRetune is an offline method that derives controller gains for a frozen policy without evaluation rollouts or gain search. It learns a port-Hamiltonian model from demonstrations to estimate effort and energy associated with the policy's predicted actions, adjusting the downstream controller while preserving the policy and its action representation. PHRetune improves Diffusion Policy success by up to 9.4 percentage points across LIBERO suites and outperforms alternative methods on real-world tasks.
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