Mechanism (biology)
Related papers: 20
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Biological mechanisms, in the context of robotics and AI, refer to the structural, functional, and computational principles observed in living organisms that inspire the design and control of artificial systems. These include muscular actuation, neural motor control, sensorimotor feedback loops, skeletal kinematics, and collective behavioral strategies found in animals and humans. In robotics, biological mechanisms inform the development of bio-inspired actuators—such as soft artificial muscles and dielectric elastomers—compliant manipulators, exoskeletons, and locomotion systems that mimic creatures ranging from octopuses to vertebrates. In AI and cognitive systems, they underpin models of motor learning, intrinsic motivation, and adaptive control drawn from neuroscience. Understanding biological mechanisms matters because natural evolution has optimized them for efficiency, robustness, and adaptability in complex, unstructured environments—qualities that remain difficult to engineer from first principles. By translating these principles into robotic hardware and algorithms, researchers can create systems that move more naturally, interact more safely with humans, and generalize more effectively across unpredictable real-world conditions.
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Top Cited Papers
Introduction to Robotics mechanics and Control
John Craig
Citations: 5039 • 1986
A unified approach for motion and force control of robot manipulators: The operational space formulation
Oussama Khatib
Citations: 2917 • 1987
Adaptive representation of dynamics during learning of a motor task
Reza Shadmehr, FA Mussa-Ivaldi
Citations: 2666 • 1994
Anthropomorphism and the social robot
B. R. Duffy
Citations: 1411 • 2003
Intrinsic Motivation Systems for Autonomous Mental Development
Pierre‐Yves Oudeyer, Frédé́ric Kaplan, Verena V. Hafner
Citations: 1113 • 2007
Hydraulically amplified self-healing electrostatic actuators with muscle-like performance
Eric Acome, Shane K. Mitchell, Timothy G. Morrissey, Madison B. Emmett, C. Benjamin, Mary E. King, M. Radakovitz, Christoph Keplinger
Citations: 1038 • 2018
Upper-Limb Powered Exoskeleton Design
Joel C. Perry, Jacob Rosén, Stephen P. Burns
Citations: 1001 • 2007
Soft Robot Arm Inspired by the Octopus
Cecilia Laschi, Matteo Cianchetti, Barbara Mazzolai, Laura Margheri, Maurizio Follador, Paolo Dario
Citations: 960 • 2012
Efficient Dynamic Computer Simulation of Robotic Mechanisms
Michael Walker, David E. Orin
Citations: 815 • 1982
What is intrinsic motivation? A typology of computational approaches
Pierre‐Yves Oudeyer
Citations: 805 • 2007
Wearable Sensors‐Enabled Human–Machine Interaction Systems: From Design to Application
Ruiyang Yin, Depeng Wang, Shufang Zhao, Zheng Lou, Guozhen Shen
Citations: 734 • 2020
<i>Theory of Machines and Mechanisms</i>
John J. Uicker, Gordon R. Pennock, Joseph Edward Shigley, J. Michael McCarthy
Citations: 698 • 2003
A Survey and Analysis of Multi-Robot Coordination
Zhi Yan, Nicolas Jouandeau, Arab Ali Chérif
Citations: 640 • 2013
Multi-functional dielectric elastomer artificial muscles for soft and smart machines
Iain A. Anderson, Todd Gisby, Thomas G. McKay, Benjamin O’Brien, Emilio P. Calius
Citations: 612 • 2012
Compliant Mechanisms: Design of Flexure Hinges
Nicolae Lobontiu
Citations: 595 • 2002
Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learning
Changan Chen, Yuejiang Liu, S. Kreiss, Alexandre Alahi
Citations: 581 • 2019
Emergent Sensing of Complex Environments by Mobile Animal Groups
Andrew M. Berdahl, Colin J. Torney, Christos C. Ioannou, Jolyon J. Faria, Iain D. Couzin
Citations: 556 • 2013
A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network
Rodney A. Brooks
Citations: 549 • 1989
The development of soft gripper for the versatile robot hand
Shigeo Hirose, Yoji Umetani
Citations: 479 • 1978
A survey on dielectric elastomer actuators for soft robots
Guoying Gu, Jian Zhu, Xiangyang Zhu
Citations: 471 • 2017