This paper presents the development of a generic gait synthesis for the humanoid robot COMAN. Based on the essential Gait Pattern Generator, the proposed synthesis offers enhanced versatilities of bipedal locomotion for different tasks and also provides the data storage and communication mechanisms among different modules. As an outcome, we are able to augment new abilities for COMAN by integrating new control modules and software tools at a cost of a very few modifications. Moreover, the integrated foot placement optimization in the Gait Pattern Generator optimizes the referential gait parameters in order to comply with the natural dynamics and kinematics of the robot, which improves the robustness of the implementation on real robots. We have also presented a practical approach to generate pelvis motion from CoM references using a simplified three-point-mass model, as well as a straightforward and effective state estimation using the sensory feedback. Three types of real experiments were studied in an increasing complexity to demonstrate the effectiveness and successful implementation of the proposed gait synthesis on a real humanoid.

This paper presents a novel online walking control that replans the gait pattern based on our proposed foot placement control using the actual center of mass (COM) state feedback. The analytic solution of foot placement is formulated based on the linear inverted pendulum model (LIPM) to recover the walking velocity and to reject external disturbances. The foot placement control predicts where and when to place the foothold in order to modulate the gait given the desired gait parameters. The zero moment point (ZMP) references and foot trajectories are replanned online according to the updated foothold prediction. Hence, only desired gait parameters are required instead of predefined or fixed gait patterns. Given the new ZMP references, the extended prediction self-adaptive control (EPSAC) approach to model predictive control (MPC) is used to minimize the ZMP response errors considering the acceleration constraints. Furthermore, to ensure smooth gait transitions, the conditions for the gait initiation and termination are also presented. The effectiveness of the presented gait control is validated by extensive disturbance rejection studies ranging from single mass simulation to a full body humanoid robot COMAN in a physics based simulator. The versatility is demonstrated by the control of reactive gaits as well as reactive stepping from standing posture. We present the data of the applied disturbances, the prediction of sagittal/lateral foot placements, the replanning of the foot/ZMP trajectories, and the COM responses.

The embodiment of physical compliance in humanoid robots, inspired by biology, improves the robustness of locomotion in unknown environments. The mechanical implementation using elastic materials demands a further combination together with controlled compliance to make the intrinsic compliance more effective. We hereby present an active compliance control to stabilize the humanoid robots for standing and walking tasks. Our actively controlled compliance is achieved via admittance control using closed-loop feedback of the 6-axis force/torque sensors in the feet. The modeling and theoretical formulation are presented, followed by the simulation study. Further, the control algorithms were validated on a real humanoid robot COMAN with inherent compliance. A series of experimental comparisons were studied, including standing balancing against impacts, straight walking, and omni-directional walking, to demonstrate the necessity and the effectiveness of applying controlled compliance on the basis of physical elasticity to enhance compliant foot-ground interaction for the successful locomotion. All data from simulations and experiments related with the proposed controller and the performance are presented, analyzed, and discussed.

A novel control synthesis is proposed for humanoids to demonstrate unique foot tilting behaviors comparable to humans in balance recovery. Our study of model based behaviors explains the underlying mechanism and the significance of foot tilting well. Our main algorithms are composed of impedance control at the center of mass, virtual stoppers that prevents over-tilting of the feet, and postural control for the torso. The proof of concept focuses on the sagittal scenario and the proposed control is effective to produce human-like balancing behaviors characterized by active foot tilting. The successful replication of this behavior on a real humanoid proves the feasibility of deliberately controlled underactuation. The experimental validation was rigorously performed, and the data from the sub-modules and the entire control were presented and analyzed.

Reasoning about contacts between a legged robot's foot and the ground is a critical aspect of locomotion in natural terrains. This interaction becomes even more critical when the robot must move on rough surfaces. This paper presents a new visual contact analysis, based on curved patches that model local contact surfaces both on the sole of the robot's foot and in the terrain. The focus is on rigid, flat feet that represent the majority of the designs for current humanoids, but we also show how the introduced framework could be extended to other foot profiles, such as spherical feet. The footholds are localized visually in the environment's point cloud through a fast patch fitting process and a contact analysis between patches on the sole of the foot and in the surrounding environment. These patches aim to compose a spatial patch map for contact reasoning. We experimentally validate the introduced visionbased framework, using range data for rough terrain stepping demonstrations on the COMAN and WALK-MAN humanoids.