Force and torque (F/T) sensing is critical for robot-environment interaction, but physical F/T sensors impose constraints in size, cost, and fragility. To mitigate this, recent studies have estimated force/wrench sensorlessly from robot internal states. While existing methods generally target relatively slow interactions, tasks involving rapid interactions, such as grinding, can induce task-critical high-frequency vibrations, and estimation in such robotic settings remains underexplored. To address this gap, we propose a Frequency-aware Decomposition Network (FDN) for short-term forecasting of vibration-rich wrench from proprioceptive history. FDN predicts spectrally decomposed wrench with asymmetric deterministic and probabilistic heads, modeling the high-frequency residual as a learned conditional distribution. It further incorporates frequency-awareness to adaptively enhance input spectra with learned filtering and impose a frequency-band prior on the outputs. We pretrain FDN on a large-scale open-source robot dataset and transfer the learned proprioception-to-wrench representation to the downstream. On real-world grinding excavation data from a 6-DoF hydraulic manipulator and under a delayed estimation setting, FDN outperforms baseline estimators and forecasters in the high-frequency band and remains competitive in the low-frequency band. Transfer learning provides additional gains, suggesting the potential of large-scale pretraining and transfer learning for robotic wrench estimation. Code and data will be made available upon acceptance.

翻译：力与力矩（F/T）感知对于机器人与环境交互至关重要，但物理F/T传感器在尺寸、成本和脆弱性方面存在限制。为缓解这一问题，近期研究通过机器人内部状态实现了无传感器力/力矩估计。尽管现有方法通常针对较慢的交互过程，但诸如磨削等涉及快速交互的任务会产生任务关键的高频振动，而此类机器人场景下的力/力矩估计仍未得到充分探索。为填补这一空白，我们提出了一种频率感知分解网络（FDN），用于从本体感受历史序列中短期预测振动丰富的力/力矩。FDN通过非对称确定性头部与概率性头部预测频谱分解的力/力矩，将高频残差建模为学习到的条件分布。此外，它引入频率感知机制，通过可学习滤波自适应增强输入频谱，并对输出施加频带先验。我们在大规模开源机器人数据集上预训练FDN，将学习到的本体感受到力/力矩的表示迁移至下游任务。在六自由度液压机械臂的真实磨削挖掘数据及延迟估计场景下，FDN在高频段优于基线估计器与预测器，并在低频段保持竞争力。迁移学习带来了额外增益，表明大规模预训练与迁移学习在机器人力/力矩估计中的潜力。代码与数据将在录用后公开。