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AnySkin: Plug-and-play Skin Sensing for Robotic Touch
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While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySkin addresses the critical challenges that impede the use of tactile sensing -- versatility, replaceability, and data reusability. Building on the simplistic design of ReSkin, and decoupling the sensing electronics from the sensing interface, AnySkin simplifies integration making it as straightforward as putting on a phone case and connecting a charger. Furthermore, AnySkin is the first uncalibrated tactile-sensor with cross-instance generalizability of learned manipulation policies. To summarize, this work makes three key contributions: first, we introduce a streamlined fabrication process and a design tool for creating an adhesive-free, durable and easily replaceable magnetic tactile sensor; second, we characterize slip detection and policy learning with the AnySkin sensor; and third, we demonstrate zero-shot generalization of models trained on one instance of AnySkin to new instances, and compare it with popular existing tactile solutions like DIGIT and ReSkin. Videos of experiments, fabrication details and design files can be found on https://any-skin.github.io/
Forward citations
Cited by 6 Pith papers
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Feel the Force: Contact-Driven Learning from Humans
FeelTheForce trains a robot policy on human tactile demonstrations, predicting desired contact forces and using a PD controller to track them on the robot gripper, achieving 77% success across five force-sensitive tasks.
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OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
A policy-agnostic two-stage real-world RL method learns tactile residual corrections on frozen visual policies, lifting contact-rich task success from 5–40% to 85–100% in under 80 minutes.
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Touch begins where vision ends: Generalizable policies for contact-rich manipulation
A localize-then-execute policy that combines vision-language reaching, semantic background augmentation, and residual reinforcement learning with tactile sensing reaches about 90% success on millimeter-precision manip...
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eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures
eFlesh is a customizable 3D-printed magnetic tactile sensor that localizes contact to 0.5 mm, estimates force within 0.27 N, and boosts precise robot manipulation success to 91%.
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Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation
A force-guided attention module and future-force prediction auxiliary task improve visuo-tactile fusion for dexterous manipulation, reaching 93% average success in real robot trials.
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The MOTIF Hand: A Robotic Hand for Multimodal Observations with Thermal, Inertial, and Force Sensors
The MOTIF hand adds thermal, inertial, and force sensing to a LEAP hand and demonstrates temperature-aware grasping and mass discrimination from fingertip flicks.
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