About this book:

This comprehensive book explores the mechanical, sensory, and algorithmic foundations required to imbue robotic hands with human-like dexterity. It begins by establishing the necessity of tactile sensing—beyond mere vision—to handle the uncertainties of contact-rich tasks like slip detection, texture recognition, and precise assembly. The text details the "anatomy" of dexterous hardware, covering the integration of diverse sensing modalities: tactile arrays (taxels and optical gels) for localized contact geometry, force/torque sensors for global interaction wrenches, and proprioceptive encoders for internal state estimation.

A significant portion of the work is dedicated to the "nervous system" of the robot: the signal conditioning, calibration, and real-time embedded computing required to process high-bandwidth sensory data. It bridges the gap between physics and control by translating the principles of contact mechanics and friction modeling into actionable control architectures. These range from foundational PID loops to advanced frameworks like Impedance, Admittance, and Hybrid Force–Position control, alongside low-latency reflexes designed to prevent drops or damage within milliseconds of a detected event.

The book emphasizes the transition from rigid programming to adaptive intelligence through data-driven methods. It provides an in-depth look at grasp planning under uncertainty, in-hand manipulation strategies like finger gaiting, and the role of supervised, self-supervised, and reinforcement learning. To support these AI approaches, the text offers practical guidance on collecting multimodal datasets and utilizing high-fidelity simulators to bridge the "Sim2Real" gap, allowing policies trained in virtual environments to function reliably on physical hardware.

The concluding chapters address the systemic requirements for deploying these technologies in the real world, focusing on hardware prototyping, power management, and standardized evaluation benchmarks. Special attention is paid to Safety and Human–Robot Interaction (HRI), ensuring that dexterous hands can serve as trustworthy collaborators. Finally, the book identifies remaining frontiers, such as achieving human-level tactile acuity and self-healing skins, setting a roadmap for the future of autonomous physical interaction.

What You'll Find Inside:

• Tactile sensing provides high-resolution contact information (pressure, shear, slip) essential for dexterous manipulation beyond vision alone.
• Force/torque and proprioception complement tactile data to form a closed-loop perception-action system for stable, adaptive control.
• Control strategies ranging from PID to model predictive control, impedance/admittance, and low-latency reflexes enable real-time response to contact events.
• Learning from tactile data via supervised, self-supervised, and reinforcement learning allows generalization to novel objects and complex in-hand maneuvers.
• Hardware design, sensor integration, embedded compute, and rigorous evaluation are critical for transferring tactile manipulation from simulation to real-world reliability and safety.

Who's It For:

This book is intended for graduate students, researchers, and engineers in robotics, haptics, and machine learning, as well as practitioners in manufacturing, logistics, assistive devices, and field robotics. A working knowledge of dynamics, control, and probability will help, but the text builds intuition before formality and provides concrete, end‑to‑end examples.