AnySkin: Plug-and-play Skin Sensing for Robotic Touch
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:W6ZALLSJrecord.jsonopen to challenge →
read the original abstract
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/
This paper has not been read by Pith yet.
Forward citations
Cited by 7 Pith papers
-
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...
-
VibeAct: Vibration to Actions for Contact-Rich Reactive Robot Dexterity
VibeAct bridges real vibro-acoustic sensing and sim-based RL via a shared contact/slip representation, outperforming proprioception baselines on contact-rich dexterous tasks with successful real-world transfer.
-
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.
-
Tac-DINO: Learning Vision-Tactile Features with Patch Alignment
Tac-DINO constructs a large tactile dataset and Vis-Tac Holographic Matching Benchmark, then proposes Vision-Tactile Patch Alignment (VTPA) methods that outperform non-aligned baselines on local-to-global feature matching.
-
RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation
RGB-S projects tactile contacts onto images as force-modulated Gaussian saliency maps via kinematics and zero-initialized conditioning, raising real-world occluded dexterous manipulation success by 26.7 percentage poi...
-
FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems
FlexiTac is a scalable piezoresistive tactile sensing system with flexible FPC-Velostat-FPC pads and a 100 Hz multi-channel readout board that mounts on rigid or soft grippers and supports visuo-tactile learning.
-
OmniUMI: Towards Physically Grounded Robot Learning via Human-Aligned Multimodal Interaction
OmniUMI introduces a multimodal handheld interface that synchronously records RGB, depth, trajectory, tactile, internal grasp force, and external wrench data for training diffusion policies on contact-rich robot manipulation.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.