mlsanityData to 7 Oct 2026

Extended Tactile Perception: Vibration Sensing through Tools and Grasped Objects

arXiv 2106.00489 ↗First posted 1 Jun 2021cs.RO cs.LGcited by 4

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Abstract (arXiv)

Humans display the remarkable ability to sense the world through tools and other held objects. For example, we are able to pinpoint impact locations on a held rod and tell apart different textures using a rigid probe. In this work, we consider how we can enable robots to have a similar capacity, i.e., to embody tools and extend perception using standard grasped objects. We propose that vibro-tactile sensing using dynamic tactile sensors on the robot fingers, along with machine learning models, enables robots to decipher contact information that is transmitted as vibrations along rigid objects. This paper reports on extensive experiments using the BioTac micro-vibration sensor and a new event dynamic sensor, the NUSkin, capable of multi-taxel sensing at 4~kHz. We demonstrate that fine localization on a held rod is possible using our approach (with errors less than 1 cm on a 20 cm rod). Next, we show that vibro-tactile perception can lead to reasonable grasp stability prediction during object handover, and accurate food identification using a standard fork. We find that multi-taxel vibro-tactile sensing at sufficiently high sampling rate led to the best performance across the various tasks and objects. Taken together, our results provides both evidence and guidelines for using vibro-tactile perception to extend tactile perception, which we believe will lead to enhanced competency with tools and better physical human-robot-interaction.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 51 references that are in the map. First posted on arXiv 1 Jun 2021.

Builds on

6 of 51 references.

References · 6

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2019End-to-End Learning of Representations for Asynchronous Event-Based Datacited by 216
  3. 2020Event-Driven Visual-Tactile Sensing and Learning for Robotscited by 27
  4. 2017Robotic Tactile Perception of Object Properties: A Reviewcited by 24
  5. 2018Towards Robotic Feeding: Role of Haptics in Fork-based Food Manipulationcited by 16
  6. 2019Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Seriescited by 10

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1.

4 papers.