Footstep Planning for the Honda ASIMO Humanoid

By Joel E. Chestnutt, Manfred Lau, German K. M. Cheung, James J. Kuffner Jr., Jessica K. Hodgins, Takeo Kanade. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

456 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/2b6333849f2ae2ae146e541d8a911ea699157bcd

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In Biomedical Engineering

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Citations in the corpus

A review of mobile robots: Concepts, methods, theoretical framework, and applications (2019, background): “In the last decade, walking and talking has been greatly improved.(45) Thinking capabilities are still their weak point and continue to pose a challenge.”
Manipulators and Manipulation in high dimensional spaces (2016, background): “After decades of research in legged locomotion, we are now capable of producing careful walking with humanoid robots [39] [2].”
From human to humanoid locomotion—an inverse optimal control approach (2010, background): “In humanoid robot research, several authors have studied real time path planning and adaption based on sensor information, looking at the same time at the shape of the path and an appropriate choice of footholds (e.g. Stasse et al. 2006; Chestnutt et al. 2005; Gutmann et al. 2005; Yoshida et al. 200”
Learning to search: structured prediction techniques for imitation learning (2009, methodology): “Using a 2-dimensional robot planner as a heuristic has been shown previously (Chestnutt et al., 2005) to dramatically improve planning performance, but the planner must be manually tuned to”
HumanPlus: Humanoid Shadowing and Imitation from Humans (2024, background): “…approaches, such as decoupling the problem into perception, planning and tracking, and separate modularization of control for arms and legs [10, 10, 23, 40], can be time-consuming to be designed and limited in scope, making them difficult to scale to the diverse range of tasks and…”
Learning to search: Functional gradient techniques for imitation learning (2009, background): “Recent work has demonstrated that decomposing legged locomotion into separate footstep planners and execution controllers is an effective strategy for many problems (Chestnutt et al., 2003; Chestnutt et al., 2005).”
Real-time behaviour synthesis for dynamic hand-manipulation (2014, background): “After decades of research in legged locomotion, we are now capable of producing careful walking with humanoid robots [4] [5].”
Boosting Structured Prediction for Imitation Learning (2006, methodology): “Using a 2-dimensional robot planner as a heuristic has been shown previously (Chestnutt et al., 2005) to dramatically improve planning performance, but the planner must be manually tuned to provide costs that serve as reasonable approximations of the true cost.”