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Digitizing Touch with an Artificial Multimodal Fingertip
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Digitizing Touch with an Artificial Multimodal Fingertip
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Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch.
Forward citations
Cited by 8 Pith papers
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SpikeATac: A Multimodal Tactile Finger with Taxelized Dynamic Sensing for Dexterous Manipulation
A fingertip with 16-taxel PVDF dynamic sensing plus capacitive static sensing enables fast delicate grasping and, with RLHF fine-tuning, in-hand manipulation of fragile objects.
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FELT: Generating Tactile Signals from Vision for Visuo-Tactile Manipulation
FELT predicts finger pressure maps from RGB images and uses them or their learned features to improve manipulation policies without real tactile sensors at deployment.
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Heterogeneous Tactile Transformer
HTT learns shared representations across heterogeneous tactile sensors using a new paired dataset and pretraining objectives, enabling transfer to unseen sensors and tasks.
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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 co...
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Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding
Contact-Grounded Policy predicts coupled robot-state and tactile trajectories with a diffusion model and maps them via a learned consistency function to executable targets for compliance controllers, outperforming sta...
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DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter
A decoupled multimodal diffusion transformer with a LoRA tactile adapter improves real-world bimanual manipulation success by 21 percentage points over a diffusion-policy baseline; a new 50-hour tactile bimanual datas...
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3D Cal: An Open-Source Software Library for Depth Reconstruction on Vision-Based Tactile Sensors
3D Cal repurposes a 3D printer as an automated calibration rig and trains a lightweight CNN, TouchNet, to reconstruct depth maps for DIGIT and GelSight Mini.
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Classification of Vision-Based Tactile Sensors: A Review
A review that proposes a four-type classification of vision-based tactile sensors, dividing them into marker-based versus intensity-based transduction with subtypes and combinations.
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