ManipForce：面向接触丰富操作任务的基于频率感知表示的力引导策略学习
ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation
AI总结：
针对接触丰富操作中视觉模仿学习缺乏力觉信息的问题，本文提出ManipForce手持采集系统与频率感知多模态Transformer（FMT），通过融合高频力-力矩和RGB数据，在六项真实任务中平均成功率83%，显著优于仅视觉基线。
摘要：
精密装配等接触丰富操作任务需要对交互力进行精确控制，然而现有模仿学习方法主要依赖仅含视觉的演示。我们提出ManipForce，这是一个手持系统，旨在自然人类演示过程中采集高频力-力矩（F/T）和RGB数据，用于接触丰富操作。基于这些演示，我们引入了频率感知多模态Transformer（FMT）。FMT使用频率和模态感知嵌入对异步RGB和F/T信号进行编码，并在Transformer扩散策略中通过双向交叉注意力将其融合。通过在六个真实世界接触丰富操作任务（如齿轮装配、翻箱和电池插入）上的大量实验，基于ManipForce演示训练的FMT在所有任务上取得了稳健的性能，平均成功率为83%，显著优于仅使用RGB的基线。消融和采样频率分析进一步证实，引入高频F/T数据和跨模态集成可以提升策略性能，尤其是在需要高精度和稳定接触的任务中。
英文摘要：
Contact-rich manipulation tasks such as precision assembly require precise control of interaction forces, yet existing imitation learning methods rely mainly on vision-only demonstrations. We propose ManipForce, a handheld system designed to capture high-frequency force-torque (F/T) and RGB data during natural human demonstrations for contact-rich manipulation. Building on these demonstrations, we introduce the Frequency-Aware Multimodal Transformer (FMT). FMT encodes asynchronous RGB and F/T signals using frequency- and modality-aware embeddings and fuses them via bi-directional cross-attention within a transformer diffusion policy. Through extensive experiments on six real-world contact-rich manipulation tasks - such as gear assembly, box flipping, and battery insertion - FMT trained on ManipForce demonstrations achieves robust performance with an average success rate of 83% across all tasks, substantially outperforming RGB-only baselines. Ablation and sampling-frequency analyses further confirm that incorporating high-frequency F/T data and cross-modal integration improves policy performance, especially in tasks demanding high precision and stable contact.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
