2025/09/23 by Geonhyup Lee, Lee, Geonhyup, Kangmin Kim +10 · 1 citation
Engineering · Neuroscience · #FOS: Computer and information sciences #Motor Control and Adaptation #Muscle activation and electromyography studies #Robot Manipulation and Learning #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2509.19047
openalex publication_date 2025/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.