Abstract Robust push recovery controllers must stabilize a system in response to various perturbations. Existing approaches based on reduced-order models can underutilize the system's capabilities. This study addresses this gap by integrating balanced state basins, computed from whole-body dynamics, into a partition-aware controller. These basins represent sets in center-of-mass state-space, from which a robot with an idealized controller can achieve a desired equilibrium state, subject to contact requirements such as step length. The basins are constructed using an optimization framework that incorporates whole-body dynamics alongside system- and task-specific constraints. Polynomial regression is used to approximate the basin as a function of step length. The parameterized basins partition the state space into regions requiring a step and those that do not, serving as a decision boundary between the non-stepping and stepping sub-controllers. The non-stepping sub-controller is designed to return the system to static equilibrium without changing contact and uses an iterative linear quadratic regulator with a single-rigid-body model for efficient trajectory optimization. The stepping sub-controller models the system dynamics as a passive 3D pendulum and uses a capture-point-based planner to achieve a stabilizing step. The combined use of these sub-controllers and basin estimation enables multi-step balance recovery with one-step planning at a time. Real-time simulations demonstrate the controller's potential to augment the computational efficiency of reduced-order models with the dynamic feasibility guarantees of full-order balanced state basins.
Bodmer et al. (Fri,) studied this question.
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