The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation

By Shuuji Kajita, Fumio Kanehiro, K. Kaneko, K. Yokoi, Hirohisa Hirukawa. Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180), 2001

1,221 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Civil and Structural Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f029b05520b668edf94fe3d1398d38ae786b10b0

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Citations in the corpus

Resolved momentum control: humanoid motion planning based on the linear and angular momentum (2003, methodology): “We generated a walking pattern based on the 3D Linear Inverted Pendulum Mode [13].”
HumanPlus: Humanoid Shadowing and Imitation from Humans (2024, background): “While model-based control [16, 36, 40, 55, 69, 92, 98] has made tremendous progress on a wide variety of humanoid robots [13, 32, 37, 54, 65, 89], learning-based meth-ods can achieve robust locomotion performance for humanoids [39, 67, 68, 85, 91, 94, 103] and biped robots [4, 42, 44, 45, 83, 84,…”
Toward fast policy search for learning legged locomotion (2012, background): “Reducing the complexity resulting from the high dimensionality of the state-action space has been at the core of most legged locomotion systems and is accomplished by smart design of either the control system [10], [25] or the mechanics [13].”
Capturability-based analysis and control of legged locomotion, Part 1: Theory and application to three simple gait models (2011, methodology): “In Sections 4 through 7 we apply the capturability framework to three simple gait models based on the Linear Inverted Pendulum Model [23,24].”
Capturability-based analysis and control of legged locomotion, Part 2: Application to M2V2, a lower-body humanoid (2012, methodology): “We can solve for a foot location that results in either desired orbital energies or, equivalently, a desired velocity vector at a given value of r′ [1], [2].”
Control of Instantaneously Coupled Systems applied to humanoid walking (2010, methodology): “If we then constrain our policy such that l̇ = 0 and c̈z = 0, (20) and (21) simplify to the well-known Linear Inverted Pendulum Model (LIPM) [15] [16].”
Feedback Control of Dynamic Bipedal Robot Locomotion (2007, methodology): “The ZMP criterion has also been used to analyze the stability of the control algorithms of [129,130,148,172].”
A reactive controller framework for quadrupedal locomotion on challenging terrain (2013, methodology): “For example, the 3D Linear Inverted Pendulum [13], the Linear Inverted Pendulum plus Flywheel [14], the Linear Inverted Pendulum with finite-size foot and reactive mass [15].”
Capture Point: A Step toward Humanoid Push Recovery (2006, methodology): “on either desired Orbital Energy, or desired speed at a given value of x [3], [4].”
OpenHRP: Open Architecture Humanoid Robotics Platform (2004, methodology): “To evaluate the walking pattern generator, we also conducted an experiment controlling a 12-DOF biped robot by using a game pad (Kajita et al. 2001b).”