Capturability-based analysis and control of legged locomotion, Part 1: Theory and application to three simple gait models

By Twan Koolen, Tomas de Boer, John R. Rebula, Ambarish Goswami, Jerry E. Pratt. Int. J. Robotics Res., 2011

509 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Genetics

View Paper: https://www.semanticscholar.org/paper/5a3c49eddc97b00d36b891ea0970d4ada8f2f9c7

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Citations in the corpus

Capturability-based analysis and control of legged locomotion, Part 2: Application to M2V2, a lower-body humanoid (2012, methodology): “In Part 1 (Koolen et al. 2012), we proposed to approach this problem using the concept of N-step capturability, informally defined as the ability to come to a stop in N steps or fewer.”
Combining model-based policy search with online model learning for control of physical humanoids (2016, background): “To date, a large number of effective approaches to controlling bipedal humanoid robots rely on either execution of pre-created movement trajectories, or manually designed controllers and regulation of control features [2], [5], [11], [20], [21], [24], [25].”
Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids (2015, background): “Examples are ZMP and inverted pendulum models – which have been very successful in walking [1], [2], [4], [9], [12].”
Models, feedback control, and open problems of 3D bipedal robotic walking (2014, background): “[155] considered a ywheel model in order to present the idea of the capture point a point on the ground on which a biped can step and come to a complete (upright) stop without falling over; additional information on capture points can be found in [96, 156].”
A reactive controller framework for quadrupedal locomotion on challenging terrain (2013, methodology): “In this paper, we propose a push recovery algorithm based on the concept of N-step capturability, described in [12].”
Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segway (2018, background): “An important feature of the pendulum models, nevertheless, is that the Cartesian velocity of their center of mass can be “stabilized by foot placement”; more precisely, by controlling the angle of the swing leg at ground contact, the robot’s speed can be regulated to a desired value [24], [28], [32]”
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots (2021, methodology): “Traditional approaches for locomotion of bipedal walking robots are typically based on notions of gait stability, such as the ZMP criterion [1] and capturability [2], simplified models [7]–[9], and constrained optimization methods [10]– [12].”