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- W2096363181 abstract "Legged robots represent great promise for transport in unstructured environments. However, it has been difficult to devise motion planning strategies that achieve a combination of energy efficiency, safety and flexibility comparable to legged animals. In this paper, we address this issue by presenting a trajectory generation strategy for dynamic bipedal walking robots using a factored approach to motion planning - combining a low-dimensional plan (based on intermittently actuated passive walking in a compass-gait biped) with a manifold learning algorithm that solves the problem of embedding this plan in the high-dimensional phase space of the robot. This allows us to achieve task level control (over step length) in an energy efficient way - starting with only a coarse qualitative model of the system dynamics and performing a data-driven approximation of the dynamics in order to synthesize families of dynamically realizable trajectories. We demonstrate the utility of this approach with simulation results for a multi-link legged robot." @default.
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- W2096363181 date "2008-05-01" @default.
- W2096363181 modified "2023-09-24" @default.
- W2096363181 title "Trajectory generation for dynamic bipedal walking through qualitative model based manifold learning" @default.
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- W2096363181 doi "https://doi.org/10.1109/robot.2008.4543234" @default.
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