Computer optimization of a minimal biped model discovers walking and running

By Manoj Srinivasan, Andy Ruina. Nature, 2006

647 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a957c3880ebdc5cec600a8a77f4de2d21a387365

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In Biomedical Engineering

In Statistical and Nonlinear Physics

Citations in the corpus

Discovery of complex behaviors through contact-invariant optimization (2012, background): “[Srinivasan and Ruina 2005; Mordatch et al. 2010] have tried to capture different modes of locomotion within a single controller, though they still assume a fixed pattern of foot contacts that are specific to locomotion.”
Optimal gait and form for animal locomotion (2009, methodology): “For instance, optimization over morphology and gait [Paul and Bongard 2001], and optimization over different gait styles [Srinivasan and Ruina 2006] have both been applied to simplified bipedal models.”
Energy-minimizing kinematics in hovering insect flight (2007, positive): “The advantage of this model, as opposed to the more computationally costly method of direct numerical simulation via computational fluid dynamics, is that it allows us to perform optimization procedures and detailed sensitivity analyses which require many cost function evaluations.”
Robust physics-based locomotion using low-dimensional planning (2010, methodology): “In biomechanics and robotics, simplified low-dimensional mechanical models have long been used to understand the essential dynamic properties of human motion [Alexander 1980; Collins et al. 2005; McGeer 1990; Srinivasan and Ruina 2006].”
Optimizing Locomotion Controllers Using Biologically-Based Actuators and Objectives (2012, background): “Our unified approach to both walking and running is consistent with the view that humans select between walking and running by minimizing energy at different speeds [Srinivasan and Ruina 2006].”
Models, feedback control, and open problems of 3D bipedal robotic walking (2014, background): “Remark 17 The important role of the optimization criterion in gait design has been extensively explored; see [193] and references therein.”
An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation (2017, background): “An example of this can be found in both [55] and [9].”
Computational principles of sensorimotor control that minimize uncertainty and variability (2007, neutral): “Forexample,successfulmodelsof walkinghavebeendevelopedbasedonminimizingenergy (Srinivasan&Ruina,2006),whileitispossibletomodel thesaccadicmainsequence,therelationbetweensaccadic amplitudeandduration,byminimizingacostwhich combinesbothmovementdurationandeyemovement variability(Harris&Wolpert,2006).”
A direct method for trajectory optimization of rigid bodies through contact (2014, background): “Slight variations from the original sequence are possible if the formulation allows the time duration of individual modes to vanish, as in [19].”
Compliant leg behaviour explains basic dynamics of walking and running (2006, background): “Hence, despite being inaccurate, the stiff-legged motion remains the mechanical paradigm for the walking gait (Dickinson et al. 2000; Srinivasan & Ruina 2005).”