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Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins
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While visuomotor policy learning has advanced robotic manipulation, precisely executing contact-rich tasks remains challenging due to the limitations of vision in reasoning about physical interactions. To address this, recent work has sought to integrate tactile sensing into policy learning. However, many existing approaches rely on optical tactile sensors that are either restricted to recognition tasks or require complex dimensionality reduction steps for policy learning. In this work, we explore learning policies with magnetic skin sensors, which are inherently low-dimensional, highly sensitive, and inexpensive to integrate with robotic platforms. To leverage these sensors effectively, we present the Visuo-Skin (ViSk) framework, a simple approach that uses a transformer-based policy and treats skin sensor data as additional tokens alongside visual information. Evaluated on four complex real-world tasks involving credit card swiping, plug insertion, USB insertion, and bookshelf retrieval, ViSk significantly outperforms both vision-only and optical tactile sensing based policies. Further analysis reveals that combining tactile and visual modalities enhances policy performance and spatial generalization, achieving an average improvement of 27.5% across tasks. https://visuoskin.github.io/
Forward citations
Cited by 5 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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LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing
LocoTouch trains a quadrupedal policy that uses a 221-taxel tactile back to balance and transport unsecured cylindrical objects, transferring zero-shot to a real Unitree Go1.
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Representation-Aligned Tactile Grounding for Contact-Rich Robotic Manipulation
Future tactile prediction applied to intermediate action-expert features, rather than visual-language or final-action features, improves contact-rich manipulation in SmolVLA and π0.
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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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