#Tactile Perception (20)

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FTP-1: A Generalist Foundation Tactile Policy

@LeoKharon · 193 views · 2026-09-14

Tactile SensingFoundation ModelsRobot Manipulation

2:15

ACE-Data-0 synchronized multimodal embodied dataset

@yukangcao · 1k views · 2026-07-31

ACEACE-Data-0Embodied AI

N₀-Foundation: Towards the Age of Tactile Intelligence

We present N₀-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale multimodal data, tactile representation learning, and standardized evaluation. First, we engineer the infrastructure for scalable data collection, including a vision-based tactile sensor, a tactile Universal Manipulation Interface (UMI), and a synchronized visuo-tactile data collection system supporting both robot embodiments and UMI-based demonstrations. Leveraging this infrastructure, we construct NeoData, which contains more than 30000 hours of synchronized visual and tactile demonstrations, spanning six embodiments, 450 tasks, and billions of paired RGB and tactile frames collected through a mixture of real-robot teleoperation and UMI-based demonstrations. To facilitate open research, we further release OpenNeoData, a 5000-hour open-source subset of NeoData. The dataset addresses a central limitation of existing manipulation corpora, critical for deformable-object manipulation, precise assembly, delicate force control, and sustained surface interaction. Capitalizing on the large-scale, heterogeneous tactile measurements, we propose NeoForce, a visuo-tactile representation model that learn transferable tactile representations across different sensor designs. To enable systematic evaluation of tactile embodied models built upon our infrastructure, datasets and tactile representations, we further propose a comprehensive benchmark, which combines the real-world NeoReal suite and the simulated NeoSim suite for standardized evaluation. Experiments across both suites show that policies benefit from the physical contact state rather than from the device-specific appearance of the tactile signal. We release the dataset, the representation, and the benchmark, aiming at supporting future work on tactile-enabled embodied manipulation.

NeoteAI Team, Fudan TEAI TeamAug 30, 2026

UMIVLATactile PerceptionAug 30, 2026

Helix 02: Figure's First Unified Whole-Body Loco-Manipulation VLA

Figure AI releases Helix 02, extending single-network control from upper body to whole robot. System 0 trained on 1000+ hours of human motion data replaces 100K lines of hand-engineered C++, enabling 4-minute continuous autonomous loco-manipulation.

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Figure AIHelixVLAloco-manipulationHumanoid

NEO's Hands: An API to the Physical World

1X announces 25-DOF tendon-driven hands for NEO. Low gear ratios enable force transparency and backdrivability, combined with high-resolution tactile skin, turning every grasp into an experiment — a perception stack, not just an actuator.

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1X1X NEObackdrivabilityDexterous HandTendon-Driven

Tac4Loco: Learning Spatiotemporal Plantar Pressure Representations for Humanoid Locomotion

Humanoid robots are expected to traverse complex terrains, where the plantar support may vary dramatically due to foot placement errors, ground properties, and transient dynamics. To achieve robust locomotion, the robots are required to adapt to uneven terrain and uncertain foot–ground interactions. Existing locomotion policies rely primarily on proprioception or exteroceptive terrain perception, where the former provides only indirect evidence of plantar support, while the latter predicts contact conditions before touchdown but cannot observe the actual support in real-time. Although some studies incorporate plantar contacts as an auxiliary perception, they rely mainly on summary statistics, overlooking the spatial topology of plantar pressure, which provides a more direct characterization of the realized contact state. To bridge this gap, we present Tac4Loco, a tactile-perceptive framework that incorporates multi-array plantar pressure as direct feedback for humanoid locomotion. We formulate a topology-preserving ordinal representation to map simulated and physical sensor signals into a shared observation space, with a dual-branch encoder for extracting their spatial and temporal representations. Subsequently, the learned spatiotemporal features are integrated with augmented proprioception including terrain estimation cues, and provided to an asymmetric actor-critic architecture for policy learning. Extensive simulation and real-world experiments demonstrate improved tracking performance and support adaptation on terrains with inclined, partial, asymmetric, and changing support. We further demonstrate its zero-shot deployment on unseen compliant and unstructured terrains, including a foam platform and a gravel road. All code and experimental configurations will be released as open-source to facilitate reproducibility.

Ziyun Liu, Sikai Guo, Zheng LiAug 16, 2026

Tac4LocoLocomotionTactileAug 16, 2026

2:32

Being-H0.8 Brings Sense of Touch to Embodied AI

@Ronald_vanLoon · 117 views · 2026-08-17

HumanoidtouchTactile Sensing

ModuTech MoVi Vision-Based Tactile Sensing Module

Vision-based tactile sensing module for embodied-AI dexterous hands and industrial assembly: 20 N max range with 0.1 N minimum trigger force, 28x20 mm sensing area (customizable), up to 120 Hz sampling, 1280x800 camera / 800x560 tactile resolution, 0.65 W typical power and a 3-million-cycle lifetime for grasp force control and contact detection.

COMPONENT

SensorsVisual-tactile sensorsTactile PerceptionEmbodied AIDexterous Hand Tactile

0:50

Robot prototype with full-body flexible electronic skin

@warmcore7278 · 136 views · 2026-08-11

Electronic skinTactile PerceptionHumanoid robots

SuperTac: Biomimetic Multimodal Tactile Sensing for Robots

Published in Nature Sensors, SuperTac is a biomimetic multimodal tactile sensor inspired by pigeon multispectral vision. It integrates multispectral imaging, triboelectric and inertial sensing in a 1mm skin, achieving 0.06N force accuracy, 0.4mm position accuracy, and 94-100% classification accuracy across texture, material, colour, sliding and collision. The DOVE 8.5B tactile language model enables natural-language tactile understanding.

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SuperTacDOVEe-skinTactile PerceptionRobot Sensor

0:58

Towards Artificial Nerves: Biomimetic Optical-Fiber Tactile Sensing for Robots

Robotic systems increasingly demand tactile sensing that approaches the adaptability and resolution of human skin to enable dexterous manipulation and safe interaction. OptiTac is a biomimetic tactile sensor that emulates the mechanoreceptor-to-nerve architecture of human touch by pairing each mechanical pin on a soft skin with an optical fiber acting as an artificial nerve. This design demonstrates an architectural principle for routing tactile information away from the sensing surface while preserving high spatial resolution, establishing a practical route toward distributed tactile sensing in future robotic systems. By treating tactile signals as images, simple analytical methods, rather than opaque deep-learning models, are used to infer contact location, size, and shape, providing interpretable and scalable tactile intelligence. This work demonstrates how evolutionary principles from biology can guide the development of artificial nerve systems for robots, offering a pathway toward human-like tactile perception in next-generation robotic platforms. More broadly, OptiTac establishes an artificial nerve-inspired sensing framework for interpretable robotic touch and a scalable route toward future distributed tactile systems.

Laura E. Butcher, Chris J. Ford, Nathan F. LeporaJul 17, 2026

Tactile SensingOptiTacBiomimeticJul 17, 2026

1:47

Electronic skin brings human-like touch to humanoid robots

@Ronald_vanLoon · 105 views · 2026-07-22

Electronic skinTactile SensingHumanoid

BayesContact: Uncertain Pose Estimation via Visuo-Tactile Proposals and Simulation-based Inference

Contact-rich manipulation requires pose estimates that are often more accurate than what depth-only sensing provides. Existing methods, relying on vision and contact, employ costly offline training procedures that need to be retrained for new environments and geometries. We propose BayesContact, a Simulation-Based Inference framework for visuo-tactile pose estimation in peg-in-hole insertion. BayesContact maintains a particle belief over object pose and fuses depth observations with force/torque-derived contact evidence. We employ simulation based forward models to approximate these observation likelihoods. For each pose hypothesis, a renderer predicts depth measurements and a physics simulator predicts contact outcomes under guarded probing actions; both are scored against real observations to update the belief. The resulting multimodal belief also enables information-gain-based probing for active disambiguation. Across simulated geometries and real-robot experiments, BayesContact improves pose observability and insertion success over vision-only inference by 30%

Aditya Kamireddypalli, Matias Mattamala, Joao MouraJul 17, 2026

TactilePose estimationSBIJul 17, 2026

VTLoc: Learning-based Tactile Contact Localization in Visual Point Clouds

Vision and touch are complementary modalities essential for robotic perception and manipulation. While vision provides global object context, touch offers precise local information at contact points. Integrating these modalities for contact localization, i.e., predicting the location of touch on an object's surface, poses significant challenges due to the need for accurate spatial alignment between tactile data and visual geometry. To address this challenge, we propose VTLoc, a novel visual-tactile framework that localizes contact points from tactile readings using a 3D point cloud as visual input. VTLoc introduces two key components: a geometric multi-modal alignment module, which reconstructs a pseudo-point cloud from fused visual-tactile features and aligns it with the visual point cloud to enforce spatial consistencies across modalities; and an iterative localizing updater, which iteratively refines the predicted contact location using fused visual-tactile features. Evaluated on a new benchmark of 100 real-world objects, VTLoc improves single-touch contact localization by reducing local-to-global correspondence ambiguity.

Zhiyuan Wu, Zhuo Chen, Shan LuoJul 17, 2026

TactilePoint cloudsContact LocalizationJul 17, 2026

AC-VLA: Robust Out-of-Distribution Action Execution via Compositional Learning

Vision-Language-Action (VLA) models excel at end-to-end robotic manipulation but struggle with out-of-distribution (OOD) generalization when familiar sub-tasks are recombined in unseen configurations. We identify two mutually reinforcing failure modes: trajectory overfitting, where models overfit to holistic trajectory patterns rather than compositional sub-skill semantics; and perceptual shortcut, where action tokens over-rely on wrist-view textures at the expense of global spatial grounding. To address both, we introduce AC-VLA, a plug-and-play Action Compositional learning framework comprising two architecture-agnostic components: (i) a compositional learning module that uses an LLM-driven instruction decomposer and a proprioceptive trajectory aligner to generate dense sub-task supervision, followed by mixed training on complete demonstrations and decomposed data to endow the model with compositional generalization; and (ii) a state-conditioned asymmetric masking strategy that suppresses wrist-view inputs during closed-gripper phases, enforcing global semantic grounding. All components are architectural modification-free and directly integrable into any VLA backbone. Instantiated on π_0.5 and evaluated on LIBERO and LIBERO-OOD benchmarks, AC-VLA achieves a ~28% absolute improvement on compositional OOD tasks while maintaining near-perfect in-distribution performance.

Xiaojiang Peng, Kai Peng, Jie LuJul 17, 2026

TactileSimulationManipulationJul 17, 2026

#Tactile Perception (20)

FTP-1: A Generalist Foundation Tactile Policy

ACE-Data-0 synchronized multimodal embodied dataset

N₀-Foundation: Towards the Age of Tactile Intelligence

Helix 02: Figure's First Unified Whole-Body Loco-Manipulation VLA

NEO's Hands: An API to the Physical World

Tac4Loco: Learning Spatiotemporal Plantar Pressure Representations for Humanoid Locomotion

Being-H0.8 Brings Sense of Touch to Embodied AI

ModuTech MoVi Vision-Based Tactile Sensing Module

Robot prototype with full-body flexible electronic skin

SuperTac: Biomimetic Multimodal Tactile Sensing for Robots

PX-FOOTRIX: robot shoes with tactile sensing for ENGINEAI T800

Daimon Robotics upgrades tactile sensor perception layer, brutal stress tests

Linker Hand O30: Dexterous Hand for Real Work

ViTacWorld: Tactile-Enhanced Robot World Model

Being-H0.8: Latent Tactile World Action Model

Towards Artificial Nerves: Biomimetic Optical-Fiber Tactile Sensing for Robots

Electronic skin brings human-like touch to humanoid robots

BayesContact: Uncertain Pose Estimation via Visuo-Tactile Proposals and Simulation-based Inference

VTLoc: Learning-based Tactile Contact Localization in Visual Point Clouds

AC-VLA: Robust Out-of-Distribution Action Execution via Compositional Learning