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Tactile-Based Robotic Manipulation: From Mechanics-Driven to Data-Driven Perspectives

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arXiv:2610.30438v1 Announce Type: new Abstract: Tactile sensing provides robots with direct access to physical interactions at the contact interface, making it a critical modality for robust and dexterous manipulation. Over the past several decades, tactile-based manipulation research has evolved from mechanics-driven approaches, which explicitly interpret tactile signals through contact mechanics and task-specific reasoning, to data-driven approaches that learn tactile representations and manipulation policies directly from data. This paper reviews the development of tactile-based manipulation from these two complementary perspectives. We first introduce the fundamentals of tactile sensing, and then review, from a mechanics-driven perspective, how those signals form manipulation-relevant…

SourcearXiv RoboticsAuthor: Jingyi Xiang, Yixuan Wang, Wenzhen Yuan
Tactile-Based Robotic Manipulation: From Mechanics-Driven to Data-Driven Perspectives
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[Submitted on 21 Oct 2026]

Title:Tactile-Based Robotic Manipulation: From Mechanics-Driven to Data-Driven Perspectives

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Abstract:Tactile sensing provides robots with direct access to physical interactions at the contact interface, making it a critical modality for robust and dexterous manipulation. Over the past several decades, tactile-based manipulation research has evolved from mechanics-driven approaches, which explicitly interpret tactile signals through contact mechanics and task-specific reasoning, to data-driven approaches that learn tactile representations and manipulation policies directly from data. This paper reviews the development of tactile-based manipulation from these two complementary perspectives. We first introduce the fundamentals of tactile sensing, and then review, from a mechanics-driven perspective, how those signals form manipulation-relevant state representations and subsequently influence the robot action. We then examine the recent advances in data-driven tactile manipulation, covering imitation learning, reinforcement learning, vision-language-action models, and predictive models. Finally, we discuss open challenges and future opportunities toward scalable, generalizable, and robust tactile-enabled robotic systems.

Comments: 30 pages. Author accepted manuscript. Posted with permission from the Annual Review of Control, Robotics, and Autonomous Systems, Volume 10; copyright 2027 the author(s), this https URL. Published version: this https URL

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Robotics (cs.RO)

Cite as: arXiv:2610.30438 [cs.RO]

(or arXiv:2610.30438v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.30438

arXiv-issued DOI via DataCite (pending registration)

Related DOI:

https://doi.org/10.1146/annurev-control-032125-030330

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From: Jingyi Xiang [view email] [v1] Wed, 21 Oct 2026 20:49:25 UTC (82 KB)

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  • AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
  • arXiv:2610.30438v1 Announce Type: new Abstract: Tactile sensing provides robots with direct access to physical interactions at the contact interface, making it a critical modality…

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