Tactile sensing advances robot learning for flexible interaction
Tactile-Guided Robot Learning: From Representation Learning to Foundation Models
Robotics
Summary
Robots use touch to better understand and interact with objects, not just to detect contact but to learn skills that work in many situations. The authors review how touch data helps robots learn representations, make decisions, and adapt to new tasks or environments. They discuss challenges like combining touch with other types of understanding and scaling these methods to different robots and hardware. This work sees touch as a key sense for robots becoming more adaptable and intelligent in their actions.
What this means in practice
- •For robotics engineers: Design tactile learning systems that generalize across different objects and robots for robust manipulation.
- •For robotic control developers: Incorporate tactile-based models to improve prediction and adaptation in robot physical interactions.
A survey. It maps existing work.
Authors
Kiyanoush Nazari, Luchen Li, Haoshu Fang, Nima Fazeli, Zhibin Li, Fumiya Iida, Thomas George Thuruthel
Abstract
Tactile sensing is becoming central to robust, adaptive, and dexterous robotic manipulation. Beyond its traditional role in contact detection and reactive control, touch is increasingly used to learn transferable representations, condition manipulation policies, and support embodied physical reasoning. Yet existing surveys largely emphasize tactile hardware and perception, leaving the rapid shift toward learning-based tactile manipulation insufficiently synthesized. This paper reviews tactile-guided skill learning from a learning-centric perspective, organized by the functional role of touch across the learning and control pipeline. We examine tactile representation and policy learning, tactile foundation models, predictive and adaptive control, and generalization across objects, tasks, sensors, embodiments, and simulation-to-real transfer. Across these directions, we identify common design principles and remaining challenges in learning transferable physical representations, integrating semantic reasoning with contact-level control, and scaling tactile learning across heterogeneous data and hardware. Finally, we outline emerging directions toward tactile foundation and world models that can predict, reason about, and adapt physical interactions. Together, this review characterizes the transition of touch from a task-specific feedback signal toward a core modality for general-purpose embodied intelligence. An accompanying database of the reviewed literature and associated resources is available at https://sites.google.com/view/touch-in-robot-learning-review.