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For multi-limbed robots, motion planning with posture and force constraintstends to be a difficult optimization problem due to nonlinearities, which alsopresent extended solve times. We propose a multi-stage optimization frameworkwith data-driven inter-stage coupling constraints to address the nonlinearity.Both clustering and evolutionary approaches to find the McCormick enveloperelaxations are used to find the problem-specific parameters. The learnedconstraints are then used in the prior stages, which provides advancedknowledge of the following stages. This leads to improved solve times andinterpretability of the results. The planner is validated through multiplewalking and climbing tasks on a 10 kg hexapod robot.

Designing Multi-Stage Coupled Convex Programming with Data-Driven McCormick Envelope Relaxations for Motion Planning