HapTile introduces a visuotactile dataset with haptic-informed teleoperation for language-conditioned contact-rich manipulation tasks and provides baseline policy benchmarks.
Tactile-force alignment in vision-language-action models for force- aware manipulation
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5representative citing papers
VT-WAM jointly predicts visual futures, tactile deformation, and actions via flow matching with Asymmetric MoT attention and contact-gated AVTAG, reporting 71.67% success on six real-world contact-rich tasks.
TacForeSight trains a force-conditioned tactile world model to predict latent dynamics and uses those predictions as anticipatory priors inside a visuo-tactile policy for real-time contact-rich manipulation.
A visuo-tactile policy learning method that exploits tactile motion correlation for contact state distinction and Mixture-of-Transformers for cross-modal fusion.
TER-DAgger uses force-prediction mismatches to trigger human corrections and residual-policy training, lifting precision-insertion success from 40.0% to 77.2% on average.
citing papers explorer
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HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning
HapTile introduces a visuotactile dataset with haptic-informed teleoperation for language-conditioned contact-rich manipulation tasks and provides baseline policy benchmarks.
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VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
VT-WAM jointly predicts visual futures, tactile deformation, and actions via flow matching with Asymmetric MoT attention and contact-gated AVTAG, reporting 71.67% success on six real-world contact-rich tasks.
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TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation
TacForeSight trains a force-conditioned tactile world model to predict latent dynamics and uses those predictions as anticipatory priors inside a visuo-tactile policy for real-time contact-rich manipulation.
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Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation
A visuo-tactile policy learning method that exploits tactile motion correlation for contact state distinction and Mixture-of-Transformers for cross-modal fusion.
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Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
TER-DAgger uses force-prediction mismatches to trigger human corrections and residual-policy training, lifting precision-insertion success from 40.0% to 77.2% on average.