Search-based planning for manipulation with motion primitives

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Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, background): “ferences are often hard to describe and anticipate without knowing where and how the robot is deployed. This makes it infeasible to manually encode (e.g. [19]) them in existing path planners a priori [7, 26, 31]. In this work we propose an algorithm for learning user preferences over trajectories th”
Learning preferences for manipulation tasks from online coactive feedback (2015, methodology): “Over the last decade many planning algorithms have been proposed, such as sampling based planners by Lavalle and Kuffner [33], and Karaman and Frazzoli [26], search based planners by Cohen et al. [10], trajectory optimizers by Schul-man et al. [50], and Zucker et al. [63] and many more [27].”
Planning and Execution using Inaccurate Models with Provable Guarantees (2020, background): “For example, motion planning usually involves using analytical motion primitives [9] that are precomputed offline and are difficult to update during online execution with-”
CHOMP: Covariant Hamiltonian optimization for motion planning (2013, methodology): “Additional work has examined inducing smoother motion while reducing the cardinality of the action set [13], as well as re-using partial plans discovered by past searches [59].”