TacVerse is a new multi-sensor tactile dataset with 106,800 images from seven VBTS designs that benchmarks within-sensor performance, zero-shot cross-sensor transfer, and few-shot adaptation on shape, grating, and force tasks.
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Sparsh: Self-supervised touch representations for vision- based tactile sensing
14 Pith papers cite this work. Polarity classification is still indexing.
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FTP-1 is the first foundation tactile policy pretrained on ~3000 hours of data from 26 sources across 21 sensors that improves performance on seen setups by 17.2% and transfers to unseen sensors with 31% success rate gain.
Touch-R1 applies GRPO reinforcement learning on a new 1M tactile dataset and benchmark to train a Qwen2.5-VL-7B model that outperforms baselines on tactile perception and visual-tactile conflict tasks.
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.
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and reorientation tasks.
Tactile-WAM with TAAM improves mean success rate by 38.9% overall and 86% on contact-rich tasks on ManiFeel by using VideoClean mask and touch-aware bias to prevent tactile pollution in world action models.
GLAM learns a shared latent action space grounded in consistent future observation prediction across heterogeneous data sources to train improved behavioral cloning policies for robot manipulation tasks.
A cross-embodiment force-position interface with system-identified torque calibration enables a flow-matching policy to perform transferable compliant grasping on heterogeneous dexterous hands.
Large multi-source tactile data plus question-guided Gaussian temporal MoE yields ~7-point gains over VTV-LLM on tactile property and commonsense reasoning tasks, with improved unseen-sensor generalization.
A vision-language-action policy that predicts future tactile images and uses that predicted touch to refine its actions reaches up to 95% success on contact-rich manipulation.
ViTacFormer learns a cross-modal visuo-tactile latent space with autoregressive tactile prediction and an easy-to-hard curriculum, then uses the representation for imitation learning that yields ~50% higher success and the first reported 11-stage, 2.5-minute autonomous dexterous tasks.
TacEvo is an LLM-driven self-evolving search method that discovers neural architectures for robotic tactile force regression and grating classification, reporting fitness gains of 56.1% and 96.1% over 20 generations.
A visuo-tactile policy learning method that exploits tactile motion correlation for contact state distinction and Mixture-of-Transformers for cross-modal fusion.
Polyurethane vision-based tactile sensors are more resilient to normal loading, shear, and abrasion than silicone ones, extending the usable force range at the cost of low-force sensitivity.
citing papers explorer
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TacVerse: A Multi-Sensor Dataset and Benchmark for Cross-Sensor Vision-Based Tactile Perception
TacVerse is a new multi-sensor tactile dataset with 106,800 images from seven VBTS designs that benchmarks within-sensor performance, zero-shot cross-sensor transfer, and few-shot adaptation on shape, grating, and force tasks.
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FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
FTP-1 is the first foundation tactile policy pretrained on ~3000 hours of data from 26 sources across 21 sensors that improves performance on seen setups by 17.2% and transfers to unseen sensors with 31% success rate gain.
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Touch-R1: Reinforcing Touch Reasoning in MLLMs
Touch-R1 applies GRPO reinforcement learning on a new 1M tactile dataset and benchmark to train a Qwen2.5-VL-7B model that outperforms baselines on tactile perception and visual-tactile conflict tasks.
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AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models
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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TactX: Learning Shared Tactile Representations Across Diverse Sensors
TactX learns a shared latent representation across three tactile sensor modalities via joint training on paired contacts, enabling zero-shot policy transfer and higher success on pick-and-place, insertion, wiping, and reorientation tasks.
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Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention
Tactile-WAM with TAAM improves mean success rate by 38.9% overall and 86% on contact-rich tasks on ManiFeel by using VideoClean mask and touch-aware bias to prevent tactile pollution in world action models.
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Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models
GLAM learns a shared latent action space grounded in consistent future observation prediction across heterogeneous data sources to train improved behavioral cloning policies for robot manipulation tasks.
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Transferring Contact, Not Just Motion: Compliant Grasping Across Dexterous Hands
A cross-embodiment force-position interface with system-identified torque calibration enables a flow-matching policy to perform transferable compliant grasping on heterogeneous dexterous hands.
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TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation
Large multi-source tactile data plus question-guided Gaussian temporal MoE yields ~7-point gains over VTV-LLM on tactile property and commonsense reasoning tasks, with improved unseen-sensor generalization.
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Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation
A vision-language-action policy that predicts future tactile images and uses that predicted touch to refine its actions reaches up to 95% success on contact-rich manipulation.
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ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation
ViTacFormer learns a cross-modal visuo-tactile latent space with autoregressive tactile prediction and an easy-to-hard curriculum, then uses the representation for imitation learning that yields ~50% higher success and the first reported 11-stage, 2.5-minute autonomous dexterous tasks.
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TacEvo: Self-Evolving Architecture Discovery for Robotic Tactile Perception via LLM-Driven Quality-Diversity Search
TacEvo is an LLM-driven self-evolving search method that discovers neural architectures for robotic tactile force regression and grating classification, reporting fitness gains of 56.1% and 96.1% over 20 generations.
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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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Characterizing the Resilience and Sensitivity of Polyurethane Vision-Based Tactile Sensors
Polyurethane vision-based tactile sensors are more resilient to normal loading, shear, and abrasion than silicone ones, extending the usable force range at the cost of low-force sensitivity.