VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation
We propose VT-MUSE, a Multimodal Unified SEquential representation learning framework for visuotactilemanipulation. Existing approaches often encode visual and tactile observations independently before fusion, limiting their ability to capture fine-grained cross-modal dependencies. Moreover, most methods focus on observations at the current time step and overlook the temporal evolution of contact. VT-MUSE addresses both limitations through a two-stage representation learning framework. In Stage I, modality specific encoders are jointly adapted via cross-modal temporal alignment and masked-view consistency. In Stage II, a conditional variational latent model processes masked visual sequenc...
| Authors | Congsheng Xu, Qiaochu Yang, Fangyuan Shi et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 August 2026 |
| Last updated at source | 6 October 2026 |
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