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Bionic Jumping of Humanoid Robot via Online Centroid Trajectory optimization and High Dynamic Motion controller

Journal of Bionic Engineering 2024年第6期21卷 2759-2778页

作者： Xiangji Wang Wei Guo Zhicheng He Rongchao Li Fusheng Zha Lining Sun State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin150001China Faculty of Computing Harbin Institute of Technology(HIT)Harbin150001China

The dynamic motion capability of humanoid robots is a key indicator for evaluating their ***,as a typical dynamic motion,is of great significance for enhancing the robot’s flexibility and terrain adaptability in unstructured ***,achieving high-dynamic jumping control of humanoid robots has become a challenge due to the high degree of freedom and strongly coupled dynamic *** idea for this paper originated from the human response process to jumping commands,aiming to achieve online trajectory optimization and jumping motion control of humanoid ***,we employ nonlinear optimization in combination with the Single Rigid Body Model(SRBM)to generate a robot’s Center of Mass(CoM)trajectory that complies with physical constraints and minimizes the angular momentum of the ***,a Model Predictive controller(MPC)is designed to track and control the CoM trajectory,obtaining the required contact forces at the robot’s ***,a Whole-Body controller(WBC)is used to generate full-body joint motion trajectories and driving torques,based on the prioritized sequence of tasks designed for the jumping *** control framework proposed in this paper considers the dynamic characteristics of the robot’s jumping process,with a focus on improving the real-time performance of trajectory optimization and the robustness of *** and experimental results demonstrate that our robot successfully executed high jump motions,long jump motions and continuous jump motions under complex working conditions.

关键词： Humanoid robots Jumping motion control Centroid trajectory optimization optimization and optimal control

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A stable method for task priority adaptation in quadratic programming via reinforcement learning

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2025年 91卷

作者： Testa, Andrea Laghi, Marco Del Bianco, Edoardo Raiola, Gennaro Hoffman, Enrico Mingo Ajoudani, Arash Leonardo SpA Via Raffaele Pieragostini 80 I-16149 Genoa Italy Ist Italiano Tecnol IIT Via San Quir 19D I-16163 Genoa Italy Univ Trento DISI Via Sommar 9 I-38123 Povo Italy Univ Lorraine CNRS INRIA LORIA F-54000 Nancy France

In emerging manufacturing facilities, robots must enhance their flexibility. They are expected to perform complex jobs, showing different behaviors on the need, all within unstructured environments, and without requiring reprogramming or setup adjustments. To address this challenge, we introduce the A3CQP, a non- strict hierarchical Quadratic Programming (QP) controller. It seamlessly combines both motion and interaction functionalities, with priorities dynamically and autonomously adapted through a Reinforcement Learning-based adaptation module. This module utilizes the Asynchronous Advantage Actor-Critic algorithm (A3C) to ensure rapid convergence and stable training within continuous action and observation spaces. The experimental validation, involving a collaborative peg-in-hole assembly and the polishing of a wooden plate, demonstrates the effectiveness of the proposed solution in terms of its automatic adaptability, responsiveness, flexibility, and safety.

关键词： optimization and optimal control Reinforcement learning Machine learning for robot control

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Online Friction Coefficient Identification for Legged Robots on Slippery Terrain Using Smoothed Contact Gradients

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 3150-3157页

作者： Kim, Hajun Kang, Dongyun Kim, Min-Gyu Kim, Gijeong Park, Hae-Won Korea Adv Inst Sci & Technol Sch Mech Aerosp & Syst Engn Humanoid Robot Res Ctr Dept Mech Engn Daejeon 34141 South Korea

This letter proposes an online friction coefficient identification framework for legged robots on slippery terrain. The approach formulates the optimization problem to minimize the sum of residuals between actual and predicted states parameterized by the friction coefficient in rigid body contact dynamics. Notably, the proposed framework leverages the analytic smoothed gradient of contact impulses, obtained by smoothing the complementarity condition of Coulomb friction, to solve the issue of non-informative gradients induced from the nonsmooth contact dynamics. Moreover, we introduce the rejection method to filter out data with high normal contact velocity following contact initiations during friction coefficient identification for legged robots. To validate the proposed framework, we conduct the experiments using a quadrupedal robot platform, KAIST HOUND, on slippery and nonslippery terrain. We observe that our framework achieves fast and consistent friction coefficient identification within various initial conditions.

关键词： Friction Robots Legged locomotion Smoothing methods optimization System identification Aerodynamics Jacobian matrices Training Robot kinematics Legged robots optimization and optimal control calibration and identification contact modeling

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Multi-Contact Inertial Parameters Estimation and Localization in Legged Robots

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第5期10卷 4730-4737页

作者： Martinez, Sergi Griffin, Robert Mastalli, Carlos Heriot Watt Univ Robot Motor Intelligence RoMI Lab Edinburgh EH14 4AS Scotland Florida Inst Human & Machine Cognit IHMC Robot Pensacola FL 32502 USA

optimal estimation is a promising tool for estimation of payloads' inertial parameters and localization of robots in the presence of multiple contacts. To harness its advantages in robotics, it is crucial to solve these large and challenging optimization problems efficiently. To tackle this, we (i) develop a multiple shooting solver that exploits both temporal and parametric structures through a parametrized Riccati recursion. Additionally, we (ii) propose an inertial manifold that ensures the full physical consistency of inertial parameters and enhances convergence. To handle its manifold singularities, we (iii) introduce a nullspace approach in our optimal estimation solver. Finally, we (iv) develop the analytical derivatives of contact dynamics for both inertial parametrizations. Our framework can successfully solve estimation problems for complex maneuvers such as brachiation in humanoids, achieving higher accuracy than conventional least squares approaches. We demonstrate its numerical capabilities across various robotics tasks and its benefits in experimental trials with the Go1 robot.

关键词： Robots Estimation Location awareness Manifolds Payloads Heuristic algorithms Legged locomotion Eigenvalues and eigenfunctions Convergence Linear matrix inequalities optimization and optimal control legged robots calibration and identification

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From Instantaneous to Predictive control: A More Intuitive and Tunable MPC Formulation for Robot Manipulators

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第1期10卷 748-755页

作者： Ubbink, Johan Viljoen, Ruan Aertbelien, Erwin Decre, Wilm De Schutter, Joris KU Leuven & Flanders MakeKU Leuven Dept Mech Engn B-3001 Leuven Belgium

Model predictive control (MPC) has become increasingly popular for the control of robot manipulators due to its improved performance compared to instantaneous control approaches. However, tuning these controllers remains a considerable hurdle. To address this hurdle, we propose a practical MPC formulation which retains the more interpretable tuning parameters of the instantaneous control approach while enhancing the performance through a prediction horizon. The formulation is motivated at hand of a simple example, highlighting the practical tuning challenges associated with typical MPC approaches and showing how the proposed formulation alleviates these challenges. Furthermore, the formulation is validated on a surface-following task, illustrating its applicability to industrially relevant scenarios. Although the research is presented in the context of robot manipulator control, we anticipate that the formulation is more broadly applicable.

关键词： Tuning Robots Linear programming Robot sensing systems Bandwidth Trajectory Costs Predictive control End effectors Actuators controller tuning model predictive control (MPC) motion control optimization and optimal control sensor-based control

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Decentralized Density control of Multi-Robot Systems Using PDE-Constrained optimization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 4045-4052页

作者： Niu, Longchen Notomista, Gennaro Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In this letter, we propose a decentralized optimal density control strategy for multi-robot systems modeled as interacting Brownian particles. The robots' density dynamics are described by the Fokker-Planck equation, and an optimal control problem is formulated and solved accordingly. The stability of the decentralized controller is mathematically proven, and validated through simulations and experiments with a team of real brushbots. The results confirm the convergence of the proposed density-based decentralized optimal controller.

关键词： Robots Mathematical models Noise Robot sensing systems Stochastic processes optimization Probability density function Location awareness Swarm robotics Convergence Distributed robot systems multi-robot systems optimization and optimal control

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Nonlinear Model Predictive control for Robotic Pushing of Planar Objects With Generic Shape

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 3006-3013页

作者： Federico, Sara Costanzo, Marco De Simone, Marco Natale, Ciro Univ Campania Luigi Vanvitelli Dipartimento Ingn I-81031 Aversa Italy

Robotic manipulation of objects in cluttered dynamic scenes is challenging for a twofold reason. Object detection and localization are complex due to partial occlusions and high variability in the object classes and manipulation in tight spaces is difficult due to potential collisions. The present letter focuses on the low-level control of the non-prehensile pushing action aimed at moving planar objects of generic shape along a given path with an assigned time law. Based on the continuous and nonlinear dynamics of the system, we propose a nonlinear model predictive controller (NMPC), which avoids the need for linearization and, thus, the hybrid dynamics arising from it. An extensive comparison with a state-of-the-art linear MPC demonstrates that the NMPC can successfully react to more general disturbances, outperforming the linear one. Experimental results confirm the effectiveness of the method in a task where a robot is required to grasp fruits in a container with other obstructing objects (shown in the attached video).

关键词： Robots Shape Splines (mathematics) Vectors Computational modeling Analytical models Trajectory Nonlinear dynamical systems Training Robot kinematics Contact modeling dexterous manipulation optimization and optimal control

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Autonomous Tail-Sitter Flights in Unknown Environments

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 1098-1117页

作者： Lu, Guozheng Ren, Yunfan Zhu, Fangcheng Li, Haotian Xue, Ruize Cai, Yixi Lyu, Ximin Zhang, Fu Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 510275 Peoples R China

Trajectory generation for fully autonomous flights of tail-sitter unmanned aerial vehicles (UAVs) presents substantial challenges due to their highly nonlinear aerodynamics. In this article, we introduce, to the best of the authors' knowledge, the world's first fully autonomous tail-sitter UAV capable of high-speed navigation in unknown, cluttered environments. The UAV autonomy is enabled by cutting-edge technologies including LiDAR-based sensing, differential-flatness-based trajectory planning and control with purely onboard computation. In particular, we propose an optimization-based tail-sitter trajectory planning framework that generates high-speed, collision-free, and dynamically-feasible trajectories. To efficiently and reliably solve this nonlinear, constrained problem, we develop an efficient feasibility-assured solver, Efficient Feasibility-assured optimization solver (EFOPT), tailored for the online planning of tail-sitter UAVs. We conduct extensive simulation studies to benchmark EFOPT's superiority in planning tasks against conventional nonlinear programming solvers. We also demonstrate exhaustive experiments of aggressive autonomous flights with speeds up to 15 m/s in various real-world environments, including indoor laboratories, underground parking lots, and outdoor parks.

关键词： Aerodynamics Planning Trajectory optimization Autonomous aerial vehicles Vehicle dynamics Real-time systems Trajectory planning Navigation Heuristic algorithms Computational modeling Aerial systems autonomous vehicle navigation motion and path planning optimization and optimal control perception and autonomy

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ASAP-MPC: an asynchronous update scheme for online motion planning with nonlinear model predictive control

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AUTONOMOUS ROBOTS 2025年第1期49卷 1-17页

作者： Dirckx, Dries Bos, Mathias Vandewal, Bastiaan Vanroye, Lander Swevers, Jan Decre, Wilm Katholieke Univ Leuven Dept Mech Engn MECO Res Team Celestijnenlaan 300 B-3001 Leuven Belgium

This paper presents a Nonlinear Model Predictive control (NMPC) update scheme targeted at motion planning for mechatronic motion systems, such as drones and mobile platforms. NMPC-based motion planning typically requires low computation times to be able to provide control inputs at the required rate for system stability, disturbance rejection, and overall performance. To achieve online NMPC updates in complex situations, works in literature typically rely on one of two approaches: attempting to reduce the solution times in NMPC by sacrificing feasibility guarantees, or allowing more time to the motion planning algorithm, which requires additional strategies to ensure robust tracking of the planned motion, e.g., state feedback. Following this second paradigm, this paper presents As-Soon-As-Possible MPC (ASAP-MPC), an asynchronous update scheme for online motion planning with optimal control that abandons the idea of having to satisfy restrictive real-time update rates and that solves the optimal control problem to full convergence. ASAP-MPC combines trajectory generation through optimal control with additional tracking control for improved robustness against disturbances and plant-model mismatch. The scheme seamlessly connects trajectories, resulting from subsequent NMPC solutions, providing a smooth and continuous overall trajectory for the motion system. This framework's applicability to embedded applications is shown on two different experiment setups where a state-of-the-art method fails to successfully navigate through a given environment: a quadcopter flying through a cluttered environment with hardware-in-the-loop simulation and a scale model truck-trailer manoeuvring in a structured physical lab environment.

关键词： Motion control Integrated planning and control Constrained motion planning optimization and optimal control

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Sampling-Based Model Predictive control Leveraging Parallelizable Physics Simulations

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第3期10卷 2750-2757页

作者： Pezzato, Corrado Salmi, Chadi Trevisan, Elia Spahn, Max Alonso-Mora, Javier Corbato, Carlos Hernandez Delft Univ Technol Cognit Robot Dept NL-2628 CD Delft Netherlands

We present a sampling-based model predictive control method that uses a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral controller (MPPI) that employs the GPU-parallelizable IsaacGym simulator to compute the forward dynamics of the robot and environment. Since the simulator implicitly defines the dynamic model, our method is readily extendable to different objects and robots, allowing one to solve complex navigation and contact-rich tasks. We demonstrate the effectiveness of this method in several simulated and real-world settings, including mobile navigation with collision avoidance, non-prehensile manipulation, and whole-body control for high-dimensional configuration spaces. This is a powerful and accessible open-source tool to solve many contact-rich motion planning tasks.

关键词： Robots Collision avoidance Predictive models Computational modeling Physics Trajectory Planning Cost function Real-time systems optimal control optimization and optimal control Contact Modeling Whole-Body Motion Planning and control Model Predictive Path Integral control

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