Walking Stabilization Using Step Timing and Location Adjustment on the Humanoid Robot, Atlas

Abstract

While humans are highly capable of recovering from external disturbances and uncertainties that result in large tracking errors, humanoid robots have yet to reliably mimic this level of robustness. Essential to this is the ability to combine traditional "ankle strategy" balancing with step timing and location adjustment techniques. In doing so, the robot is able to step quickly to the necessary location to continue walking. In this work, we present both a new swing speed up algorithm to adjust the step timing, allowing the robot to set the foot down more quickly to recover from errors in the direction of the current capture point dynamics, and a new algorithm to adjust the desired footstep, expanding the base of support to utilize the center of pressure (CoP)-based ankle strategy for balance. We then utilize the desired centroidal moment pivot (CMP) to calculate the momentum rate of change for our inverse-dynamics based whole-body controller. We present simulation and experimental results using this work, and discuss performance limitations and potential improvements.

References in corpus (2)

• An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion
• Walking on Partial Footholds Including Line Contacts with the Humanoid Robot Atlas

Cited by in corpus (4)

• Straight-Leg Walking Through Underconstrained Whole-Body Control
• Adaptive Non-linear Centroidal MPC with Stability Guarantees for Robust Locomotion of Legged Robots
• Online Non-linear Centroidal MPC for Humanoid Robots Payload Carrying with Contact-Stable Force Parametrization
• MOB-Net: Limb-modularized Uncertainty Torque Learning of Humanoids for Sensorless External Torque Estimation