With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart–Table (C-T) methods are straightforward and linear but inadequate for robots with flexible joints and linkages. To overcome this limitation, we propose a Flexible MPC (FMPC) framework that incorporates joint dynamics modeling and emphasizes bounded gait control to enable humanoid robots to achieve stable motion in various conditions. The FMPC is based on an enhanced flexible C-T model as the motion model, featuring an elastic layer and an auxiliary second center of mass (CoM) to simulate joint systems. The flexible C-T model’s inversion derivation allows it to be effectively transformed into the predictive equation for the FMPC, therefore enriching its flexible dynamic behavior representation. We further use the Zero Moment Point (ZMP) velocity as a control variable and integrate multiple constraints that emphasize CoM constraint, embed explicit bounded constraint, and integrate ZMP constraint, therefore enabling the control of model flexibility and enhancement of stability. Since all the above constraints are shown to be linear in the control variables, a quadratic programming (QP) problem is established that guarantees that the CoM trajectory is bounded. Lastly, simulations validate the effectiveness of the proposed method, emphasizing its capacity to generate bounded CoM/ZMP trajectories across diverse conditions, underscoring its potential to enhance gait control. In addition, the validation of the simulation of real robot motion on the robots CASBOT and Openloong, in turn, demonstrates the effectiveness and robustness of our approach.

Multimanipulators exhibit excellence across various domains through collaboration, yet optimizing motion performance to fulfill task-specific requirements while tackling unknown kinematic models remains formidable challenges. This paper pioneers the integration of game theory into collaborative control for multi-manipulators with unknown kinematics, establishing a robust optimal framework through efficient distributed networks and strategic competition to achieve Nash equilibrium. An optimal collaboration scheme integrates hybrid multi-objective optimization and orthogonal projection repetitive motion generation (OPRMG) scheme to mitigate excessive and abrupt changes in joint velocity, enhance manipulability, and eliminate joint drift while decoupling errors between joint and Cartesian spaces. Furthermore, an adaptive power-exponentialtype neural network (APETNN) robust controller is proposed to solve the scheme, integrating learning and control components to enable manipulators to respond promptly with an optimal strategy even with incomplete or implicit kinematics. The efficacy, practicality, and superiority of the proposed framework for collaborative control of multimanipulators are demonstrated through theoretical analyses, simulations and experiments.

Recent advances in bioinspired robotics highlight the growing demand for dexterous, adaptive control strategies that allow robots to interact naturally, safely, and efficiently with dynamic, contact-rich environments. Yet, achieving robust adaptability and reflex-like responsiveness to unpredictable disturbances remains a fundamental challenge. This paper presents a bioinspired imitation learning framework that models human adaptive dynamics to jointly acquire and generalize motion and force skills, enabling compliant and resilient robot behavior. The proposed framework integrates hybrid force–motion learning with dynamic response mechanisms, achieving broad skill generalization without reliance on external sensing modalities. A momentum-based force observer is combined with dynamic movement primitives (DMPs) to enable accurate force estimation and smooth motion coordination, while a broad learning system (BLS) refines the DMP forcing function through style modulation, feature augmentation, and adaptive weight tuning. In addition, an adaptive radial basis function neural network (RBFNN) controller dynamically adjusts control parameters to ensure precise, low-latency skill reproduction, and safe physical interaction. Simulations and real-world experiments confirm that the proposed framework achieves human-like adaptability, robustness, and scalability, attaining a competitive learning time of 5.56 s and a rapid generation time of 0.036 s, thereby demonstrating its efficiency and practicality for real-time applications and offering a lightweight yet powerful solution for bioinspired intelligent control in complex and unstructured environments.

Industrial surface inspection is fundamental to advanced manufacturing, yet reliable robotic image acquisition in complex geometries remains challenging due to severe occlusions and the inherent trade-off between resolution and coverage. Inspired by human visual inspection behaviors and perception–action coordination mechanisms, this paper proposes a hierarchical trajectory optimization framework for robotic image acquisition based on measured point clouds. Specifically, a multi-constraint preprocessing model is developed to emulate human-like active perception strategies, enabling occlusion-aware viewpoint generation over complex concave and convex surfaces with adaptive camera orientation. Building upon this, a multi-objective trajectory optimization method is introduced to coordinate global coverage and local motion efficiency, jointly optimizing viewpoint sequencing, path length, and motion smoothness hierarchically. To further enhance flexibility in constrained environments, a Pose Reachability Augmented Generative Adversarial Network (PRAGAN) is proposed to learn feasible and adaptable imaging postures under kinematic constraints. Experimental results on an industrial robotic platform equipped with 2D and 3D vision systems demonstrate 100% coverage of key surface areas, a 47.0% reduction in path length, and a 37.5% decrease in solution time compared with the baseline in the physical experiments, while ensuring collision-free operation. Both simulation and real-world experiments validate that the proposed framework effectively captures human-inspired perception and motion coordination, providing a practical and scalable solution for complex industrial surface inspection.

In the published publication [...].