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In this research, a biped robot is programmed to learn to walk using genetic algorithms. The biped robot has the ability to rise after a fall. The system is made according to human genetics, where a child tries to learn to walk, takes a few steps, falls, and then stands up somehow after a fall. After this process, the system restarts and tries to walk again. The biped robot evolves after each generation, and after each generation it will have a more natural walking posture. The system is unpredictable, because the biped robot sometimes evolves faster and sometimes evolves slower, like in human genetics where some children are faster and other children are slower to walk. The biped robot is considered to have evolved enough after having a walking posture similar to that of a human and does not fall.

Bipedal robots have gained increasing attention for their human-like mobility which allows them to work in various human-scale environments. However, their inherent instability makes it difficult to control their balance while they are physically interacting with the environment. This study proposes a novel balance controller for bipedal robots based on a behavior cloning model as one of the machine learning techniques. The behavior cloning model employs two deep neural networks (DNNs) trained on human-operated balancing data, so that the trained model can predict the desired wrench required to maintain the balance of the bipedal robot. Based on the prediction of the desired wrench, the joint torques for both legs are calculated using robot dynamics. The performance of the developed balance controller was validated with a bipedal lower-body robotic system through simulation and experimental tests by providing random perturbations in the frontal plane. The developed balance controller demonstrated superior performance with respect to resistance to balance loss compared to the conventional balance control method, while generating a smoother balancing movement for the robot.

This paper modifies the single rigid body (SRB) model, and considers the swinging leg as the disturbances to the centroid acceleration and rotational acceleration of the SRB model. This paper proposes deep reinforcement learning (DRL)-based model predictive control (MPC) to resist the disturbances of the swinging leg. The DRL predicts the swing leg disturbances, and then MPC gives the optimal ground reaction forces according to the predicted disturbances. We use the proximal policy optimization (PPO) algorithm among the DRL methods since it is a very stable and widely applicable algorithm. It is an on-policy algorithm based on the actor–critic framework. The simulation results show that the improved SRB model and the PPO-based MPC method can accurately predict the disturbances of the swinging leg to the SRB model and resist the disturbance, making the locomotion more robust.

Linear inverted pendulum model (LIPM) is an effective and widely used simplified model for biped robots. However, LIPM includes only the single support phase (SSP) and ignores the double support phase (DSP). In this situation, the acceleration of the center of mass (CoM) is discontinuous at the moment of leg exchange, leading to a negative impact on walking stability. If the DSP is added to the walking cycle, the acceleration of the CoM will be smoother and the walking stability of the biped will be improved. In this paper, a linear pendulum model (LPM) for the DSP is proposed, which is similar to LIPM for the SSP. LPM has similar characteristics to LIPM. The dynamic equation of LPM is also linear, and its analytical solution can be obtained. This study also proposes different trajectory-planning methods for different situations, such as periodic walking, adjusting walking speed, disturbed state recovery, and walking terrain-blind. These methods have less computation and can plan trajectory in real time. Simulation results verify the effectiveness of proposed methods and that the biped robot can walk stably and flexibly when combining LIPM and LPM.

Programming robots for performing different activities requires calculating sequences of values of their joints by taking into account many factors, such as stability and efficiency, at the same time. Particularly for walking, state of the art techniques to approximate these sequences are based on reinforcement learning (RL). In this work we propose a multi-level system, where the same RL method is used first to learn the configuration of robot joints (poses) that allow it to stand with stability, and then in the second level, we find the sequence of poses that let it reach the furthest distance in the shortest time, while avoiding falling down and keeping a straight path. In order to evaluate this, we focus on measuring the time it takes for the robot to travel a certain distance. To our knowledge, this is the first work focusing both on speed and precision of the trajectory at the same time. We implement our model in a simulated environment using q-learning. We compare with the built-in walking modes of an NAO robot by improving normal-speed and enhancing robustness in fast-speed. The proposed model can be extended to other tasks and is independent of a particular robot model.

In this paper, a novel method to determine the center of mass position of each link of human-like multibody biped robots is proposed. A first formulation to determine the total center of mass position has been tested in other works on a biped platform with human-like dimensions. In this paper, the formulation is optimized and extended, and it is able to give as output the center of mass positions of each link of the platform. The calculation can be applied to different types of robots. The optimized formulation is validated using a simulated biped robot in MATLAB.

Bipedal robots have gained increasing attention for their human-like mobility which allows them to work in various human-scale environments. However, their inherent instability makes it difficult to control their balance while they are physically interacting with the environment. This study proposes a novel balance controller for bipedal robots based on a behavior cloning model as one of the machine learning techniques. The behavior cloning model employs two deep neural networks (DNNs) trained on human-operated balancing data, so that the trained model can predict the desired wrench required to maintain the balance of the bipedal robot. Based on the prediction of the desired wrench, the joint torques for both legs are calculated using robot dynamics. The performance of the developed balance controller was validated with a bipedal lower-body robotic system through simulation and experimental tests by providing random perturbations in the frontal plane. The developed balance controller demonstrated superior performance with respect to resistance to balance loss compared to the conventional balance control method, while generating a smoother balancing movement for the robot.