To achieve walking and push recovery successfully, a biped robot must be able to determine if it can maintain its current contact configuration or transition into another one without falling. In this study, the ability of a humanoid robot to maintain single support (SS) or double support (DS) contact and to achieve a step are represented by balanced and steppable regions, respectively, as proposed partitions of an augmented center-of-mass-state space. These regions are constructed with an optimization method that incorporates full-order system dynamics, system properties such as kinematic and actuation limits, and contact interactions with the environment in the two-dimensional sagittal plane. The SS balanced, DS balanced, and steppable regions are obtained for both experimental and simulated walking trajectories of the robot with and without the swing foot velocity constraint to evaluate the contribution of the swing leg momentum. A comparative analysis against one-step capturability, the ability of a biped to come to a stop after one step, demonstrates that the computed steppable region significantly exceeds the one-step capturability of an equivalent reduced-order model. The use of balanced regions to characterize the full balance capability criteria of the system and benchmark controllers is demonstrated with three push recovery controllers. The implemented hip–knee–ankle controller resulted in improved stabilization with respect to decreased foot tipping and time required to balance, relative to an existing hip–ankle controller and a gyro balance feedback controller.

The performance of humanoid robots is improving, owing in part to their participation in robot games such as the DARPA Robotics Challenge. Along with the 2018 Winter Olympics in Pyeongchang, a Skiing Robot Competition was held in which humanoid robots participated autonomously in a giant slalom alpine skiing competition. The robots were required to transit through many red or blue gates on the ski slope to reach the finish line. The course was relatively short at 100 m long and had an intermediate-level rating. A 1.23 m tall humanoid ski robot, ‘DIANA’, was developed for this skiing competition. As a humanoid robot that mimics humans, the goal was to descend the slope as fast as possible, so the robot was developed to perform a carved turn motion. The carved turn was difficult to balance compared to other turn methods. Therefore, ZMP control, which could secure the posture stability of the biped robot, was applied. Since skiing takes place outdoors, it was necessary to ensure recognition of the flags in various weather conditions. This was ensured using deep learning-based vision recognition. Thus, the performance of the humanoid robot DIANA was established using the carved turn in an experiment on an actual ski slope. The ultimate vision for humanoid robots is for them to naturally blend into human society and provide necessary services to people. Previously, there was no way for a full-sized humanoid robot to move on a snowy mountain. In this study, a humanoid robot that transcends this limitation was realized.

For successful push recovery in response to perturbations, a humanoid robot must select an appropriate stabilizing action. Existing approaches are limited because they are often derived from reduced-order models that ignore system-specific aspects such as swing leg dynamics or kinematic and actuation limits. In this study, the formulation of capturability for whole-body humanoid robots is introduced as a partition-based approach in the augmented center-of-mass (COM)-state space. The 1-step capturable boundary is computed from an optimization-based method that incorporates whole-body system properties with full-order nonlinear system dynamics in the sagittal plane including contact interactions with the ground and conditions for achieving a complete stop after stepping. The 1-step capturable boundary, along with the balanced state boundaries, are used to quantify the relative contributions of different strategies and contacts in maintaining or recovering balance in push recovery. The computed boundaries are also incorporated as explicit criteria into a partition-aware push recovery controller that monitors the robot’s COM state to selectively exploit the ankle, hip, or captured stepping strategies. The push recovery simulation experiments demonstrated the validity of the stability boundaries in fully exploiting a humanoid robot’s balancing capability through appropriate balancing actions in response to perturbations. Overall, the system-specific capturability with the whole-body system properties and dynamics outperformed that derived from a typical reduced-order model.

Objective: Current methods for measuring metabolic energy expenditure (MEE) constrain experiment design and only provide time-averaged values. We propose a two-stage predictive model using surrogate learners in the first stage for physiological dynamics and gradient-boosted regression trees in the second stage to learn a generalized representation of instantaneous, whole-body MEE. Methods: Kinematic, kinetic, metabolic, and surface electromyograph data were recorded for nine human subjects in level, overground walking at 100%, 70%, 85%, 115%, and 130% subject-preferred speeds.We use surrogate learners to encode fundamental information about the time-varying properties of MEE. A novel machine learning model was then trained on the surrogate functions’ outputs. For robustness, an information-theoretic data selection step was added during model training. The trained model uses joint torques and angular velocities to predict instantaneous, whole-body MEE during walking. Results: The model accurately predicts instantaneous MEE without subject-specific input parameters. Shapley Additive Explanations were used to investigate energetic features of the learned MEE function and demonstrate alignment with literature.We find similarity between the model’s MEE predictions, muscle mechanical work rate, and normal ground reaction forces, suggesting a link between MEE and the work required to raise the center of mass. Conclusion: The proposed approach provides an alternative to experimental MEE measurement while balancing the generalizability and complexity trade-off typically imposed on existing, computational, predictive models. Significance: Evaluating MEE of human motion can provide insight into underlying biomechanics and inform clinical and engineering practices.

Robotic tasks often exceed the scope of steady-state or periodic behavior, which necessitates generally-applicable models of actuators intended to generate transient or aperiodic motion. However, existing electromechanical models of servomotors typically omit consideration of the switching power converter circuits required for directional, speed, or torque control. In this study, a multi-domain framework is established for switched electromechanical dynamics in servomotor systems for their analysis and control in general aperiodic tasks including transient phases. The switched electromechanical dynamics is derived from the individual models of the internal DC motor, gear train, and H-bridge circuit. The coupled models comprehensively integrate all possible distinct switching configurations of on-state, off-state, and dead time. A combination of cycle averaging with piecewise analytical solutions of the non-smooth dynamics is introduced to handle different temporal scales from high-frequency electrical to low-frequency mechanical variables. System parameters were estimated from experimental data using a dual-servomotor test platform. The model was validated for predictive accuracy against measured data in two distinct tasks—dynamic braking of a pendulum system and sinusoidal trajectory following. The model was also used to formulate the servomotor power consumption, which was implemented for optimal control demonstration and energy analysis. In particular, the servomotor power consumption model provided true optimality (minimization) when compared with the squared rotor torque and the positive rotor mechanical power that are commonly used as proxy models. While the focus of this work is on permanent-magnet, armature-controlled brushed DC servomotors, the approach is applicable to general electromechanical systems with switching-based control.