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

Year: 2026

Type: conference-paper

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.

Source: Proceedings - IEEE International Conference on Robotics and Automation/Proceedings

DOI: 10.1109/icra57385.2026.11696811

Authors: Geonhyup Lee, Youngjin Lee, Kangmin Kim, Seongju Lee, Sangjun Noh, Seunghyeok Back, Kyoobin Lee

Institutions: Gwangju Institute of Science and Technology, Korea Institute of Machinery & Materials

Language: English

Topic: Robot Manipulation and Learning

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4417314736

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