Huaiguang Yang

In this article, a novel Lyapunov-based event-trigger mechanism is proposed to reduce the computation cost of model predictive control (MPC) algorithm for discrete-time nonlinear input-affine safety-critical systems. Unlike conventional approaches that require continuous error monitoring, the proposed mechanism leverages the predictive capability of MPC to determine triggering instants directly based on the evolution of the closed-loop Lyapunov function. Safety and stability are enforced by incorporating control barrier functions (CBFs) and control Lyapunov functions (CLFs) as constraints within the MPC optimization. Furthermore, the recursive feasibility of the proposed event-triggered MPC algorithm is rigorously analyzed, with special attention to the potential infeasibility caused by hard CBF constraints. Input-to-state practical stability (ISpS) of the resulting closed-loop system is also established. Simulation results demonstrate that the proposed event-triggered CBF-CLF-MPC algorithm effectively eliminates unnecessary controller updates, reducing computational consumption while maintaining tracking performance comparable to that of a conventional time-triggered MPC algorithm.

Legged robots are designed to perform operational tasks in complex, rugged terrains. These tasks generally encompass two fundamental modalities: locomotion and manipulation. Humanoid robots, benefiting from their distinctive morphological configuration (independent arm and leg structures), can decouple the control of these two task types. In contrast, conventional quadruped/hexapod robots, lacking dedicated end effectors, are typically optimized for locomotion. While manipulation capabilities can be added via auxiliary robotic arms, this approach leads to unnecessary energy consumption and sacrifices payload capacity. Adopting a limb reuse architecture offers a promising solution. This work proposes Legs-as-Arms, a multitask reinforcement learning framework for hexapod robots that enables simultaneous locomotion and manipulation. The framework implements effective limb reuse, enabling any leg(s) to track desired trajectories while maintaining stable body movement. By leveraging a multihead critic network and value normalization, we integrate three key capabilities into a single control policy: 1) base locomotion control; 2) stationary whole-body manipulation; and 3) dynamic mobile manipulation. Our method demonstrates fault-tolerant gaits, workspace expansion, centimeter-level trajectory tracking accuracy, and significant load adaptability. We validate the framework’s effectiveness and practical application value through comprehensive hardware experiments across diverse multimodal scenarios.

Outdoor wheeled mobile manipulators dynamically interact with both manipulated objects and the underlying terrain, which may experience wheel-induced shearing and flow. Terramechanics models describe wheel-terrain interaction stresses precisely, but fail to show the wheel tractive mechanics intrinsically. Through experimental investigation, this paper establishes a linear relationship between wheel driving torque and traction across terrains of varying deformability. We conceptualize wheel-induced soil flow as comprising two components: a rim-adherent synchronous rotation flow that extends the effective lever arm of drawbar pull, and a counter-directional shear flow that induces wheel slowdown. This framework elucidates velocity and traction losses from particle displacement. A unified wheel traction model is proposed, incorporating fluctuations induced by wheel lugs. Experimental validation confirms the model's accuracy, while its online parameter identification enhances superior adaptation efficiency to terrain variations compared to terra-mechanics models. Based on this framework, speed, traction, and energy losses due to terrain deformation are precisely predicted, thereby facilitating an analytical loss function for wheeled mobile manipulator optimization. The model also demonstrates potential for traction control applications in wheeled mobile platforms.

Legged robots face significant challenges in navigating complex environments, as they require precise real-time decisions for foothold selection and contact planning. While existing research has explored methods to select footholds based on terrain geometry or kinematics, a critical gap remains: few existing methods efficiently validate the existence of a non-collision swing trajectory. This article addresses this gap by introducing KCFRC, a novel approach for efficient foothold reachability analysis. We first formally define the foothold reachability problem and establish a sufficient condition for foothold reachability. Based on this condition, we develop the KCFRC algorithm, which enables robots to validate foothold reachability in real time. Our experimental results demonstrate that KCFRC achieves remarkable time efficiency, completing foothold reachability checks for a single leg across 900 potential footholds in an average of 2 ms. Furthermore, we show that KCFRC can accelerate trajectory optimization and is particularly beneficial for contact planning in confined spaces, enhancing the adaptability and robustness of legged robots in challenging environments.

Contact planning for multi-legged robots is a challenging sequential decision-making problem due to the interplay of gaits, footholds, configurations, and physical constraints from both the robot and the environment. Existing multi-contact planners often fail to find feasible sequences within a limited time in complex scenarios and to ensure physical possibility. We propose a parallel Monte Carlo Tree Search (MCTS)-based planner that leverages multi-constraint reachability to efficiently generate physically valid contact sequences. The method accelerates planning through a hash-driven parallel approach, prioritizing promising candidates while pruning trapped nodes via valueless node evaluation. It employs depth-first backup for long-horizon planning and uses virtual loss to balance parallel exploration. To ensure feasible transitions between contact states, we establish comprehensive reachability conditions for multi-legged robots, incorporating stability, collision avoidance, kinematics, joint torques, and contact constraints into the planning framework. In experiments in sparse foothold environments, our planner outperforms mainstream contact planning approaches in traversability, solution quality and physical feasibility, while achieving a competitive planning speed. Furthermore, simulation and hardware validation on hexapod and humanoid robots exhibit successful locomotion across various terrains while satisfying constraints.

Agile quadruped robots are ideal for performing heavy-load transportation tasks in industrial settings; however, the uncertainty of external disturbances and distribution shifts pose significant challenges to system stability. We propose a complex dynamics modeling method based on the liquid neural operator (LNO), which achieves over 92% accuracy in dynamics prediction across various unknown environments. The high-fidelity dynamics model generated by LNO is controlled by a novel deep model predictive control (DMPC) method, which–to our knowledge–represents the first successful application of Koopman operator theory to real-world dynamic control of legged robots. The DMPC is equivalent to a constrained convex quadratic programming problem through state variable reconstruction. Moreover, we design a deep reinforcement learning-MPC (RL-MPC) hierarchical control architecture based on LNO, where embedding the LNO into RL policy exploration establishes a new paradigm for bridging the sim-to-real gap through operator-theoretic feature learning, enabling the robot to demonstrate high adaptability under changes in load or environment. The proposed method improves the load-to-weight ratio of the Unitree A1 robot to 1.25, and it is capable of withstanding multiple impacts with a 15 kg load. Experimental results demonstrate that the comprehensive load-bearing capacity of the A1 remarkably surpasses the State-of-the-Art achievements.