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.
hub
Omnivtla: Vision- tactile-language-action model with semantic-aligned tactile sensing
24 Pith papers cite this work. Polarity classification is still indexing.
24
Pith papers citing it
hub tools
citation-role summary
background 4
citation-polarity summary
roles
background 4representative citing papers
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.
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.
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.
Introduces H-Tac human tactile-action dataset and TTP pre-training that unifies spaces and predicts future tactile signals to improve robotic dexterous manipulation transfer.
UniTac is the first unified multimodal model for cross-sensor tactile understanding and generation, using dual-level representations, two new understanding tasks, and a two-stage training paradigm with sensor-prior sampling to achieve SOTA understanding and realistic cross-sensor generation.
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.
T-Rex introduces a large tactile dataset and MoT architecture that achieves over 30% higher success rates than baselines on 12 tasks requiring force control and deformable object handling.
TORL-VLA couples a tactile wrench-aware VLA policy with a lightweight online RL module and an intervention-censored critic to improve success and efficiency on contact-rich robotic tasks.
Dream-Tac unifies visual and tactile signals in a world action model using contact-gated fusion and attention bias, reporting 31.7% average action accuracy gains on six manipulation tasks.
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.
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.
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.
TacCoRL integrates tactile feedback into VLA policies via real-aligned simulation co-training and RL, raising average success from 50% to 72.5% on four bimanual contact-rich tasks with direct real-robot transfer.
Presents arm-worn AetheRock hardware for multi-modal data collection and ForceVT learning method to improve tactile inference robustness despite sensor variations.
MorphoQuant proposes DABC and MDQFO for 4-bit quantization of omni-modal LLMs, claiming superior performance over SOTA W4A4 methods and even W4A16 baselines on benchmarks like ScienceQA.
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.
Degradation-Driven Prompting improves VLM accuracy on deceptive visual tasks by downsampling inputs and routing them through classification, external visual tools, and a critic.
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.
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.
citing papers explorer
-
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.
-
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.
-
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.
-
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.
-
Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation
Introduces H-Tac human tactile-action dataset and TTP pre-training that unifies spaces and predicts future tactile signals to improve robotic dexterous manipulation transfer.
-
UniTac: A Unified Multimodal Model for Cross-Sensor Tactile Understanding and Generation
UniTac is the first unified multimodal model for cross-sensor tactile understanding and generation, using dual-level representations, two new understanding tasks, and a two-stage training paradigm with sensor-prior sampling to achieve SOTA understanding and realistic cross-sensor generation.
-
TAP-VLA: Tactile Annotation Prompting for Vision Language Action Models
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.
-
T-Rex: Tactile-Reactive Dexterous Manipulation
T-Rex introduces a large tactile dataset and MoT architecture that achieves over 30% higher success rates than baselines on 12 tasks requiring force control and deformable object handling.
-
TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation
TORL-VLA couples a tactile wrench-aware VLA policy with a lightweight online RL module and an intervention-censored critic to improve success and efficiency on contact-rich robotic tasks.
-
Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
Dream-Tac unifies visual and tactile signals in a world action model using contact-gated fusion and attention bias, reporting 31.7% average action accuracy gains on six manipulation tasks.
-
Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language
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.
-
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.
-
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.
-
Event-VLA: Action-Conditioned Event Fusion for Robust Vision-Language-Action Model
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.
-
TacCoRL: Integrating Tactile Feedback into VLA via Simulation
TacCoRL integrates tactile feedback into VLA policies via real-aligned simulation co-training and RL, raising average success from 50% to 72.5% on four bimanual contact-rich tasks with direct real-robot transfer.
-
AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning
Presents arm-worn AetheRock hardware for multi-modal data collection and ForceVT learning method to improve tactile inference robustness despite sensor variations.
-
MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models
MorphoQuant proposes DABC and MDQFO for 4-bit quantization of omni-modal LLMs, claiming superior performance over SOTA W4A4 methods and even W4A16 baselines on benchmarks like ScienceQA.
-
InvariantCloud: A Globally Invariant, Uniquely Indexed Point Cloud Framework for Robust 6-DoF Tactile Pose Tracking
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: Learning to Feel and Act via Contact-Driven Flow Matching
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.
-
E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes
Degradation-Driven Prompting improves VLM accuracy on deceptive visual tasks by downsampling inputs and routing them through classification, external visual tools, and a critic.
-
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.
-
Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms
A survey proposing a hierarchical taxonomy for multimodal tactile fusion datasets and methods across perception, generation, and interaction in embodied intelligence.
-
Towards Robotic Dexterous Hand Intelligence: A Survey
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 Technical Report
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.