{"ID":2867172,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19047","arxiv_id":"2509.19047","title":"ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation","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.","short_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 de...","url_abs":"https://arxiv.org/abs/2509.19047","url_pdf":"https://arxiv.org/pdf/2509.19047v1","authors":"[\"Geonhyup Lee\",\"Yeongjin Lee\",\"Kangmin Kim\",\"Seongju Lee\",\"Sangjun Noh\",\"Seunghyeok Back\",\"Kyoobin Lee\"]","published":"2025-09-23T14:15:19Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}