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arXiv · 2509.19047

ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation

Geonhyup Lee·Yeongjin Lee·Kangmin Kim·Seongju Lee·Sangjun Noh·Seunghyeok Back·Kyoobin Lee
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Abstract

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.

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Geonhyup Lee, Yeongjin Lee, Kangmin Kim, Seongju Lee, Sangjun Noh, Seunghyeok Back, Kyoobin Lee. 2025-09-23. ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation. https://arxiv.org/abs/2509.19047

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