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.
Tactile-conditioned diffu- sion policy for force-aware robotic manipulation
9 Pith papers cite this work. Polarity classification is still indexing.
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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.
Tac-DINO constructs a large tactile dataset and Vis-Tac Holographic Matching Benchmark, then proposes Vision-Tactile Patch Alignment (VTPA) methods that outperform non-aligned baselines on local-to-global feature matching.
MiTaS fuses multi-resolution tactile data from GelSight and Evetac sensors with vision using modality-specific stems and transformer fusion to condition flow-matching policies, reporting 80% average success on five contact-rich tasks versus 31-54% baselines.
TAMEn supplies a cross-morphology wearable interface and pyramid-structured visuo-tactile data regime that raises bimanual manipulation success rates from 34% to 75% via closed-loop collection.
TacImag framework trains on paired visuotactile data to predict tactile observations from vision, improving performance on six simulated and four real-world manipulation tasks.
OmniUMI introduces a multimodal handheld interface that synchronously records RGB, depth, trajectory, tactile, internal grasp force, and external wrench data for training diffusion policies on contact-rich robot manipulation.
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
citing papers explorer
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TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance
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.
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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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Tac-DINO: Learning Vision-Tactile Features with Patch Alignment
Tac-DINO constructs a large tactile dataset and Vis-Tac Holographic Matching Benchmark, then proposes Vision-Tactile Patch Alignment (VTPA) methods that outperform non-aligned baselines on local-to-global feature matching.
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Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
MiTaS fuses multi-resolution tactile data from GelSight and Evetac sensors with vision using modality-specific stems and transformer fusion to condition flow-matching policies, reporting 80% average success on five contact-rich tasks versus 31-54% baselines.
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TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks
TAMEn supplies a cross-morphology wearable interface and pyramid-structured visuo-tactile data regime that raises bimanual manipulation success rates from 34% to 75% via closed-loop collection.
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Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations
TacImag framework trains on paired visuotactile data to predict tactile observations from vision, improving performance on six simulated and four real-world manipulation tasks.
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OmniUMI: Towards Physically Grounded Robot Learning via Human-Aligned Multimodal Interaction
OmniUMI introduces a multimodal handheld interface that synchronously records RGB, depth, trajectory, tactile, internal grasp force, and external wrench data for training diffusion policies on contact-rich robot manipulation.
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World Action Models: The Next Frontier in Embodied AI
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
- Learning Versatile Humanoid Manipulation with Touch Dreaming