Stepping strategy, including step time and step location modulation, and hip strategy, i.e., upper-body movement, play crucial roles in achieving robust humanoid locomotion. However, exploiting these balance strategies in a unified and flexible manner has not been well addressed. In this article, we propose a sequential convex optimization approach. Based on the linear inverted pendulum model, we modulate step parameters, including step location and step time, using quadratically constrained quadratic programming in real time. Then, based on the nonlinear inverted pendulum plus flywheel model, we regulate angular momentum using the linear model predictive control. To accommodate for scenarios with height variation, we consider nonlinear 3-D locomotion dynamics explicitly. The proposed approach is validated via comparison studies and extensive experiments on the humanoid with planar and linear feet. The results demonstrate enhanced robustness against dynamic disturbances and adaptability to real-world scenarios. On average, the enhanced stepping strategy rejects 135% larger external forces than our previous article. Also, robust locomotion across height-varying stepping stones is realized, which is rarely reported for a humanoid robot with planar feet.

In this paper, we propose a method for improving the human operator's arm posture during bilateral teleoperation. The method is based on a musculoskeletal model that considers human operator's arm dynamics and the feedback force from the haptic interface (master), which is used to control a robotic arm (slave) in a remote environment. We perform an online optimisation to find the optimal configuration that has the longest endurance time with respect to muscle fatigue. Next, a trajectory is generated on the haptic interface in order to guide the human arm into the optimal configuration. The teleoperation is temporarily suspended by decoupling the master from the slave robot when the haptic device is being reconfigured. Afterwards, the loop is coupled again and the slave robot is controlled from the position where it stopped after the haptic interface guided the operator's arm to the optimised configuration. The main advantage of the proposed method is that the human operator can perform the task with less effort, which increases the endurance time. To validate our approach, we performed proof-of-concept experiments on a teleoperation system composed of two Franka Emika robots, where one was serving as master and the other as slave.

In this paper, we propose a method for selective monitoring and management of human muscle fatigue in human-robot co-manipulation scenarios. The proposed approach uses a machine learning technique to learn the complex relationship between individual human muscle forces, arm configuration and arm endpoint force that are provided by a sophisticated offline musculoskeletal model. The estimated muscle forces are used in the fatigue model to estimate the individual muscle fatigue levels online. Two fatigue management protocols are proposed that enable the robot to handle and reduce the human fatigue by altering the configuration of task execution. The first protocol uses optimisation technique to find the optimal position for task execution, where the fatigue-related endurance time can be maximised. The second protocol divides the arm muscles into groups and then alters the direction of endpoint force so that the fatigued muscle group can relax and the relaxed muscle group becomes active. The proposed method has a potential to enable the robot to facilitate safer and more ergonomic working conditions for the human coworker. The main advantage of this approach is that it can operate online, and that all the measurements can be performed by the robot sensory system, which can significantly increase the applicability in real world scenarios. To validate the proposed method, we performed multiple experiments with two collaborative tasks (polishing and drilling) under different conditions.