For humans, fast, efficient walking over flat ground represents the vast majority of locomotion that an individual experiences on a daily basis, and for an effective, real-world humanoid robot the same will likely be the case. In this work, we propose a locomotion controller for efficient walking over near-flat ground using a relatively simple, model-based controller that utilizes a novel combination of several interesting design features including an ALIP-based step adjustment strategy, stance leg length control as an alternative to center of mass height control, and rolling contact for heel-to-toe motion of the stance foot. We then present the results of this controller on our robot Nadia, both in simulation and on hardware. These results include validation of this controller’s ability to perform fast, reliable forward walking at 0.75 m/s along with backwards walking, side-stepping, turning in place, and push recovery. We also present an efficiency comparison between the proposed control strategy and our baseline walking controller over three steady-state walking speeds. Lastly, we demonstrate some of the benefits of utilizing rolling contact in the stance foot, specifically the reduction of necessary positive and negative work throughout the stride.

The ability to accomplish manipulation and locomotion tasks in the presence of significant time-varying external loads is a remarkable skill of humans that has yet to be replicated convincingly by humanoid robots. Such an ability will be a key requirement in the environments we envision deploying our robots: dull, dirty, and dangerous. External loads constitute a large model bias, which is typically unaccounted for. In this work, we enable our humanoid robot to engage in loco-manipulation tasks in the presence of significant model bias due to external loads. We propose an online estimation and control framework involving the combination of a physically consistent extended Kalman filter for inertial parameter estimation coupled to a whole-body controller. We showcase our results both in simulation and in hardware, where weights are mounted on Nadia’s wrist links as a proxy for engaging in tasks where large external loads are applied to the robot.

Mobility aids such as canes, crutches, and walkers are widely used among the elderly and people with poor balance as a means for physical support to improve balance during walking. Advances in technology have led to the development of robotic walking aids which can provide active physical support and navigation by incorporating sensors and actuators in conventional walking aids. These devices have shown great potentials in enhancing mobility; however, few studies have employed the functionality to detect user's posture or have investigated the feedback approaches to augment this information. Thus, it is important for those with impaired balance not to just be passively supported by mobility aids but to also actively be engaged in correcting their posture. In this paper, we introduce the concept of a sensory augmentation walker that can provide real-time directional information via skin stretch feedback to the user. The design and the user study of perceiving directions on a novel skin stretch handle are presented. Results show that the directional cues rendered by skin stretch feedback can be accurately perceived by all healthy young subjects (n = 8) at their fingertips, while the palm is shown to be a less effective location for perceiving this kind of feedback. Positive feedback about the benefits in helping people with improper posture is also reported. Based on the results of this pilot study, a full system for improving balance performance in elderly or people with impaired balance will be undertaken.