外观
论文arXiv 2610.0575523 天前
面向操作策略的演示校准 Port-Hamiltonian 重调优
原文链接：Demonstration Calibrated Port-Hamiltonian Retuning for Manipulation Policies（arXiv 2610.05755 · 发布日期：2026-10-05）
Demonstration Calibrated Port-Hamiltonian Retuning for Manipulation Policies
针对部署控制器增益选择问题，PHRetune从演示学习Port-Hamiltonian模型，离线推导增益，并在LIBERO与真实任务验证。
核心要点
- 下游阻抗控制器的刚度和阻尼增益影响成功率，但常沿用数据采集时设置。
- PHRetune将策略预测与演示推得的力和能量预算比较，闭式得到单一增益尺度。
- 方法在评估前固定增益，不需增益搜索、机械臂模型、奖励、重训或运行时额外计算。
- 在LIBERO中Diffusion Policy最高提升9.4个百分点；四个真实任务及两种VLA也有改进。
验证范围与局限：验证范围包括LIBERO suites、四个真实世界操作任务，以及Diffusion Policy、SmolVLA和OpenVLA-OFT。
英文摘要 (Abstract)
Diffusion and VLA policies for manipulation are often deployed through downstream impedance controllers. The stiffness and damping gains of these controllers affect task success, yet are commonly inherited from data collection rather than selected for the deployed policy. Although empirical gain sweeps can improve performance, they require repeated evaluation rollouts. We introduce PHRetune, an offline method that derives controller gains for a frozen policy without evaluation rollouts or gain search. Our approach learns a port-Hamiltonian model from demonstrations to estimate the effort and energy associated with the policy's predicted actions. The policy is applied to recorded demonstration observations, and its predictions are assessed against demonstration-derived effort and energy budgets. From this comparison, we derive a single gain scale in closed form, adjusting the downstream controller while preserving the policy and its action representation. The gains are fixed before evaluation, without requiring a prior manipulator model, task rewards, policy retraining, or additional runtime computation. Across LIBERO suites, PHRetune improves Diffusion Policy success by up to 9.4 percentage points, with the derived gains achieving the highest observed success rates in empirical gain sweeps. On all four real-world manipulation tasks, PHRetuned Diffusion Policy outperforms the nominal policy, alternative gain-tuning methods, and a policy-retraining baseline. The same procedure improves success with SmolVLA and OpenVLA-OFT on every task, while reducing acceleration and jerk for both VLA backbones.
作者：Yulong Yang, Fan Wu, Christine Allen-Blanchette, Amit Chakraborty
来源
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