SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection
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arXiv:2609.14783v1 Announce Type: cross Abstract: Robots need touch to manipulate objects safely and reliably, as many properties, such as softness, texture, and contact stability, are hard to infer...
arXiv:2603. 14604v2 Announce Type: replace-cross Abstract: We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models.
arXiv:2606. 31451v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities.
arXiv:2608.29601v1 Announce Type: cross Abstract: We present $\mathcal{N}_0$-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale m...
VT-MUSE is a multimodal unified sequential representation learning framework for visuotactile manipulation. It uses a two‑stage approach: first, modality‑specific encoders are jointly adapted with cross‑modal temporal alignment and masked‑view consistency; second, a conditional variational latent model processes masked visual sequences and full tactile histories, with auxiliary decoders reconstructing recent visual observations and predicting tactile depth changes. The resulting representation is fed into a lightweight Transformer policy via gated cross‑attention, achieving an 11‑percentage‑point improvement over the strongest baseline in simulation and significant gains in real‑world experiments.
arXiv:2606. 31694v1 Announce Type: cross Abstract: For robots manipulating open-world objects, tactile representations must generalize to unseen materials.