AT-VLA proposes adaptive tactile injection and a dual-stream tactile reaction mechanism to enhance VLA models for contact-rich robotic manipulation with real-time responses.
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Omnivtla: Vision- tactile-language-action model with semantic-aligned tactile sensing
14 Pith papers cite this work. Polarity classification is still indexing.
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TouchGuide improves contact-rich robot manipulation by steering diffusion or flow-matching visuomotor policies with tactile feasibility scores from a contrastively trained Contact Physical Model.
TAP-VLA improves VLA performance in contact-rich manipulation by visually annotating tactile shear fields onto input images, reaching 78% success versus under 50% for vision-only and other tactile methods.
Tabero supplies a data pipeline that turns existing robot trajectories into vision-tactile-language tasks and a VTLA model that keeps task success high while cutting average grip force by over 70 percent under gentle instructions.
TER-DAgger improves robotic precision insertion success rates by over 37% via residual policies from edited trajectories and force-aware intervention triggers.
A visuo-tactile policy learning method that exploits tactile motion correlation for contact state distinction and Mixture-of-Transformers for cross-modal fusion.
Event-VLA integrates event streams into VLA models through action-conditioned gated cross-attention to maintain performance in normal light while improving success rates under low-light and near-dark conditions.
InvariantCloud registers marker-based point clouds in one shot via global invariance to deliver drift-free 6-DoF tactile pose tracking with improved yaw accuracy over prior methods.
ForceFlow improves success rates by 37% on six real-world contact-rich tasks over ForceVLA by treating force as a global regulatory signal in a flow-matching policy with hierarchical vision-to-force decomposition.
A survey proposing a hierarchical taxonomy for multimodal tactile fusion datasets and methods across perception, generation, and interaction in embodied intelligence.
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.
RLDX-1 outperforms frontier VLAs such as π0.5 and GR00T N1.6 on dexterous manipulation benchmarks, reaching 86.8% success on ALLEX humanoid tasks versus around 40% for the baselines.