MidasTouch: Monte-Carlo inference over distributions across sliding touchDownload PDF

16 Jun 2022, 10:45 (modified: 15 Nov 2022, 16:36)CoRL 2022 OralReaders: Everyone
Student First Author: yes
Keywords: Tactile perception, Localization, 3D deep learning
TL;DR: Tracking the pose distribution of a robot finger on an object surface over time, using surface geometry captured by a tactile sensor
Abstract: We present MidasTouch, a tactile perception system for online global localization of a vision-based touch sensor sliding on an object surface. This framework takes in posed tactile images over time, and outputs an evolving distribution of sensor pose on the object's surface, without the need for visual priors. Our key insight is to estimate local surface geometry with tactile sensing, learn a compact representation for it, and disambiguate these signals over a long time horizon. The backbone of MidasTouch is a Monte-Carlo particle filter, with a measurement model based on a tactile code network learned from tactile simulation. This network, inspired by LIDAR place recognition, compactly summarizes local surface geometries. These generated codes are efficiently compared against a precomputed tactile codebook per-object, to update the pose distribution. We further release the YCB-Slide dataset of real-world and simulated forceful sliding interactions between a vision-based tactile sensor and standard YCB objects. While single-touch localization can be inherently ambiguous, we can quickly localize our sensor by traversing salient surface geometries. Project page: https://suddhu.github.io/midastouch-tactile/
Supplementary Material: zip
Website: https://suddhu.github.io/midastouch-tactile/
Code: https://github.com/facebookresearch/MidasTouch
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