Bipedal robots have long been a focal point of robotics research with an unwavering emphasis on platform stability. Achieving stability necessitates meticulous design considerations at every stage, encompassing resilience against environmental disturbances and the inevitable wear associated with various tasks. In pursuit of these objectives, here, the bipedal L04 Robot is introduced. The L04 Robot employs a groundbreaking approach by compactly enclosing the hip joints in all directions and employing a coupled joint design. This innovative approach allows the robot to attain the traditional 6 degrees of freedom in the hip joint while using only four motors. This design not only enhances energy efficiency and battery life but also safeguards all vulnerable motor reducers. Moreover, the double-slider leg design enables the robot to simulate knee bending and leg height adjustment through leg extension. This simulation can be mathematically modeled as a linear inverted pendulum (LIP), rendering the L04 Robot a versatile platform for research into bipedal robot motion control. A dynamic analysis of the bipedal robot based on this structural innovation is conducted accordingly. The design of motion control laws for forward, backward, and lateral movements are also presented. Both simulation and physical experiments corroborate the excellent bipedal walking performance, affirming the stability and superior walking capabilities of the L04 Robot.

This paper presents an exciting and meaningful design to make mobile robots capable of adapting to various terrains. We designed a relatively simple and novel composite motion mechanism called the flexible spoked mecanum (FSM) wheel and created a mobile robot, LZ-1, with multiple motion modes based on the FSM wheel. Based on the motion analysis of the FSM wheel, we designed an omnidirectional motion mode for this robot, allowing it to move flexibly in all directions and successfully traverse rugged terrains. In addition, we designed a crawl motion mode for this robot, which can climb stairs effectively. We used a multilayer control method to move the robot according to the designed motion modes. Multiple experiments showed that these two motion modes for the robot are effective on various terrains.

This paper presents the development, modeling, and control of L03, an underactuated 3D bipedal robot with symmetrical hips and straight legs. This innovative design requires only five actuators, two for the legs and three for the hips. This paper is divided into three parts: (1) mechanism design and kinematic analysis; (2) trajectory planning for the center of mass and foot landing points based on the Divergent Component of Motion (DCM), enabling lateral and forward walking capabilities for the robot; and (3) gait stability analysis through prototype experiments. The primary focus of this study is to explore the application of underactuated symmetrical designs and determine the number of motors required to achieve omnidirectional movement of a bipedal robot. Our simulation and experimental results demonstrate that L03 achieves simple walking with a stable and consistent gait. Due to its lightweight construction, low leg inertia, and straight-legged design, L03 can achieve ground perception and gentle ground contact without the need for force sensors. Compared to existing bipedal robots, L03 closely adheres to the characteristics of the linear inverted pendulum model, making it an invaluable platform for future algorithm research.

In this paper, a biped robot system for dynamic walking is presented. It has two 2-degree-of-freedom (DOF) lightweight legs and a 6-DOF hip. All the joint pulleys of the legs are driven by motors that are placed at the hip using steel cables. Since all the heavy motors are mounted at the hip, the biped robot has remarkably low-mass legs beyond the hip, which guarantees low inertia during walking at high speeds. Utilizing cable-amplification mechanisms, high stiffness and strength are achieved, resulting in better control performance compared to conventional direct-driven methods. Techniques are developed to estimate joint-angle errors caused by the elastic deformation of the cables. To achieve smooth control, we introduce the concept of a virtual leg, which is an imaginary leg connecting the hip joint and the ankle joint. A robust control approach based on the “virtual leg” is presented, which considers the variances of the virtual leg length during walking. Experiments are conducted to validate the effectiveness of the mechanical design and the proposed control approach.

This paper addresses the fundamental challenge of achieving stable and efficient walking in a lightweight, underactuated humanoid robot that lacks an ankle roll degree of freedom. To tackle this relevant critical problem, we present a hierarchical optimization framework that combines model predictive control (MPC) with a tailored whole body planner and controller (WBPC). At the high level, we employ a matrix exponential (ME)-based discretization of the MPC, ensuring numerical stability across a wide range of step sizes (5 to 100 ms), thereby reducing computational complexity without sacrificing control quality. At the low level, the WBPC is specifically designed to handle the unique kinematic constraints imposed by the missing ankle roll DOF, generating feasible joint trajectories for the swing foot phase. Meanwhile, a whole body control (WBC) strategy refines ground reaction forces and joint trajectories under full-body dynamics and contact wrench cone (CWC) constraints, guaranteeing physically realizable interactions with the environment. Finally, a position–velocity–torque (PVT) controller integrates feedforward torque commands with the desired trajectories for robust execution. Validated through walking experiments on the MuJoCo simulation platform using our custom-designed lightweight robot X02, this approach not only improves the numerical stability of MPC solutions, but also provides a scientifically sound and effective method for underactuated humanoid locomotion control.

Achieving omnidirectional walking for bipedal robots is considered one of the most challenging tasks in robotics technology. Reinforcement learning (RL) methods have proved effective in bipedal walking tasks. However, most existing methods use state machines to switch between multiple policies and achieve omnidirectional gait, which results in shaking during the policy switching process for bipedal robots. To achieve a seamless transition between omnidirectional gait and transient motion for full-size bipedal robots, we propose a novel multi-agent RL method. Firstly, a multi-agent RL algorithm based on the actor–critic framework is designed, and policy entropy is introduced to improve exploration efficiency. By learning agents with parallel initial state distributions, we minimize reliance on gait planner effectiveness in the Robot Operating System (ROS). Additionally, we design a novel heterogeneous policy experience replay mechanism based on Euclidean distance. Secondly, considering the periodicity of bipedal robot walking, we develop a new periodic gait function. Including periodic objectives in the policy can accelerate the convergence speed of training periodic gait functions. Finally, to enhance the robustness of the policy, we construct a novel curriculum learning method by discretizing Gaussian distribution and incorporate it into the robot’s training task. Our method is validated in a simulation environment, and the results show that our method can achieve multiple gaits through a policy network and achieve smooth transitions between different gaits.