A robot in the future may initially has a good learning capability but an empty library of movements. It gradually enriches its library of movements through human demonstrations. Dynamic Movement Primitives (DMPs) has been proved to be an effective way to represent trajectories. Trajectories are classified into discrete and rhythmic ones, and parameters are set for each demonstrated trajectory. However, what kind of trajectory will be provided by robot users is sometimes unknown to robot developers, so trajectory pattern and the parameters can not be determined in advance. It's also impossible for non-technical robot users to set these parameters and determine the pattern of movements they are going to demonstrate. To make it easier for non-expert robot users to programme their robots by demonstration, this work presents an efficient way to deal with these two problems. The effectiveness of the proposed methodology is proved by teaching a robot to clean the whiteboard in different ways and stack a set of cubic boxes in specific order.

To actively assist human walking and balance recovery, a unified active assistance control framework of the hip exoskeleton is proposed in this paper. At the beginning of this paper, the condition of active assistance is analyzed. And then, a novel virtual stiffness model is proposed based on the analysis of human hip joint torque during walking and balance recovery. The virtual stiffness model is utilized to generate the desired active assistance torque profile for active walking and balance assistance. Next, the unified active assistance control framework is established based on the virtual stiffness model. Finally, the effectiveness of the proposed control framework is demonstrated by walking experiments. The results of the forward walking experiment show that the muscle effort of iliopsoas is reduced and the amplitude of hip joint motion is enlarged with the assistance of hip exoskeleton. The walking forward pull experiments and the walking backward pull experiments show that when the human suffers from a forward or backward disturbance force during forward walking, exoskeleton can help human regain balance faster and can enlarge the margin of stability (MoS) of the human.

In this paper, we propose an Energy based Fall Prediction (EFP) which observes the real-time balance status of a humanoid robot during standing. The EFP provides an analytic and quantitative measure of the level of balance. Both simulation and experimental studies were conducted and compared with the previously proposed indicators, such as Capture Point (CP) and Foot Rotation Indicator (FRI). The EFP also suggests the balance augmentation by active foot tilting to create larger potential barriers. As a proof of concept, a hybrid balance controller was designed to stabilize the robot including under-actuation phases so the robot can also balance with shoes. Our study reveals that both EFP and CP successfully predict falling about 0.2s in advance for the tested robot, while the FRI fails due to the light weight of the foot and limited resolution of the force/torque measurement.

In this paper, we resolve the issue of stepping over a large obstacle by exploiting the redundancy of pelvis rotation and the versatility of foot trajectories for the humanoids. The control framework consists of a motion pattern that exploits the redundancy of pelvis rotation to enlarge the kinematic workspace, a generic foot trajectory generation which can be modified by a parametric interface to adapt to a specific task as well as utilizing the hip abduction to avoid obstacle collision. Moreover, the compensation strategies are also presented for reducing the discrepancies to implement the dynamic stepping motion on a real robot. The effectiveness is validated by COMAN's capability of dynamically stepping over a large obstacle of 10cm height by 5cm width which is almost 20% of its leg length in both simulation and experiment.

In this paper, a joint control algorism was proposed to implement on our musculoskeletal robot. The joints are all actuated by pneumatic muscles and have antagonistic structure. In order to gain a better performance, we first modeled the pneumatic muscle, and then considering the dynamics two control method was discussed and compared. As a tradeoff of rapidity and accuracy, the combined joint control algorism was implemented on the robot system. The algorism could tune the stiffness of the joint automatically to fit the compliance need for joint control. The experiments on our robot showed that the joint control algorism could operate the robot with a fast and accurate response. There is nearly not lagging behind, and with a small overshot during the step testing, and a complex leg's trajectory was also achieved through our joint controller.

The high speed running of legged robot raise higher requirements on the design and development of the robot and the control method. The mammals' excellent performance, especially the cheetah, provides us much inspiration on control, mechanical design and etc. In this paper, a model of the cheetah robot is developed for researching the locomotory behaviors of fast running. Under the biological knowledge on neural mechanism, a new bio-inspired control method was proposed via controlling the muscles flexion and extension movements. Then, a bio-musculoskeletal hindlimb prototype was designed in this paper. The muscle-like actuator and the cheetah-like structure combining the bio-control approach provide more natural fashion for the new robot. Finally, the simulation of the bounding gait establishes good biological properties, cheetah-like leg's trajectory. Experiment with the real hindlimb prototype validates the design and the development of the structure and the controller. In the near future, a final prototype will be manufactured with onboard power and air source.

In this paper, we introduce a quadruped robot designed for bounding gait with only one actuator per compliant leg. Under the analysis of the dynamics model of the robot, a new simple linear running controller using the energy control idea, which requires minimal task level feedback and only controls both the leg torque and ending impact angle, is proposed. It successfully executes fast running from rest till a constant speed and hi-speed turning, both in the prototype simulation and robot experiment. These results contribute to that complex dynamically dexterous tasks may be controlled via simple energy control method and delayed task feedback, which is closer to the animal's actual locomotion conditions. In the future, we plan to modify this method for reducing the energy expending and make the robot running fast.