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- W2161819990 abstract "In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to low-cost trajectories even when initialized with infeasible ones. It uses Hamiltonian Monte Carlo to alleviate the problem of convergence to high-cost local minima (and for probabilistic completeness), and is capable of respecting hard constraints along the trajectory. We present extensive experiments with CHOMP on manipulation and locomotion tasks, using seven-degree-of-freedom manipulators and a rough-terrain quadruped robot." @default.
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- W2161819990 date "2013-08-01" @default.
- W2161819990 modified "2023-10-18" @default.
- W2161819990 title "CHOMP: Covariant Hamiltonian optimization for motion planning" @default.
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- W2161819990 doi "https://doi.org/10.1177/0278364913488805" @default.
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