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A person practicing parkour is an incredible display of intelligent planning; he must reason carefully about his velocity and contact placement far into the future in order to locomote quickly through an environment. We seek to develop planners that will enable robotic systems to replicate this performance. An ideal planner can learn from examples and formulate feasible full-body plans to traverse a new environment. The proposed approach uses momentum equivalence to reduce the full-body system into a simplified one. Low-dimensional trajectory primitives are then composed by a sampling planner called Sampled Composition A* to produce candidate solutions that are adjusted by a trajectory optimizer and mapped to a full-body robot. Using primitives collected from a variety of sources, this technique is able to produce solutions to an assortment of simulated locomotion problems.

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