General-purpose household robots require real-time fine motor control to handle delicate tasks and urgent situations. In this work, we introduce the senses of proprioception, kinesthesia, force haptics, and muscle activation to capture such precise control. This comprehensive set of multimodal senses naturally enables fine-grained interactions that are difficult to simulate with unimodal or text con-ditioned generative models. To effectively simulate fine-grained multisensory actions, we develop a feature learning paradigm that aligns these modalities while preserving the unique information each modality provides. We further regularize action trajectory features to enhance causality for representing intricate interaction dynamics. Experiments show that incorporating multimodal senses improves simulation accuracy and reduces temporal drift. Extensive ablation studies and downstream applications demonstrate effectiveness and practicality of our work.