Unified Multimodal Diffusion Forcing for Forceful Manipulation

2025-11-06

AI Summary

Standard imitation learning typically models a unidirectional “vision → action” mapping, neglecting the dynamic coupling among multimodal sensory inputs (e.g., vision), actions, and rewards—limiting understanding and generalization of high-contact manipulation behaviors. To address this, we propose a multimodal diffusion forcing framework that jointly models spatiotemporal dependencies across RGB images, joint-level actions, and six-dimensional force-torque signals. Our key innovation is a stochastic partial masking mechanism enabling cross-modal completion, force prediction, and latent state inference. We employ diffusion models to learn the joint multimodal distribution. Evaluated on high-contact robotic tasks—including peg insertion,拔-out, and screw tightening—in both simulation and real-world settings, our method significantly outperforms baselines. It demonstrates robustness under sensor noise and supports diverse inference paradigms, including zero-shot action generation and reconstruction of missing modalities.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB images) to actions. However, such methods often overlook the rich interplay between different modalities, i.e., sensory inputs, actions, and rewards, which is crucial for modeling robot behavior and understanding task outcomes. In this work, we propose Multimodal Diffusion Forcing, a unified framework for learning from multimodal robot trajectories that extends beyond action generation. Rather than modeling a fixed distribution, MDF applies random partial masking and trains a diffusion model to reconstruct the trajectory. This training objective encourages the model to learn temporal and cross-modal dependencies, such as predicting the effects of actions on force signals or inferring states from partial observations. We evaluate MDF on contact-rich, forceful manipulation tasks in simulated and real-world environments. Our results show that MDF not only delivers versatile functionalities, but also achieves strong performance, and robustness under noisy observations. More visualizations can be found on our website https://unified-df.github.io

Problem

• Learning robot manipulation from multimodal data beyond action generation
• Modeling temporal and cross-modal dependencies in contact-rich tasks
• Achieving robust performance under noisy sensory observations

Innovation

• Unified multimodal diffusion model for robot trajectories
• Random partial masking trains cross-modal dependencies
• Reconstructs trajectories for forceful manipulation tasks